Interannual probabilistic forecast of the SST anomalies. a–c Mean prediction of SST anomaly for the next 5 years for three different averaging times: annual, 2, and 5 years; horizontal black lines correspond to the mean, ±1, and ±2 standard deviations of the climatological distribution. Circles and squares represent mean predictions with coefficient of determination bigger and smaller than 0.2; colorscale represents the mean prediction of the SST anomaly; the vertical colored lines represent ±1 standard deviation of prediction distributions. Stars denote the most likely state from the distributions. d–f Prediction of SST distribution (in percent) for 1, 2, and 5 years in advance with respect to 2017. Gray histograms in the background represent the asymptotic, climatological distribution; vertical blue lines represent the current position used to initialize the forecast system; vertical black lines correspond to the mean, ±1, and ±2 standard deviations of the climatological distribution. g–i Distribution of probability anomaly (in percent), probability changes with respect to the climatological distribution, for the 1, 2, and 5 years in advance predictions. Background colorscale represents ±0–1, 1–2, and more than 2 standard deviations, respectively, consistently with moderate, intense, and extreme events. Graphic: Sévellec and Sybren Drijfhout, 2018 / Nature Communications

PARIS, 14 August 2018 (CNRS) – This summer's world-wide heatwave makes 2018 a particularly hot year. As will be the next few years, according to a study led by Florian Sévellec, a CNRS researcher at the Laboratory for Ocean Physics and Remote Sensing (LOPS) (CNRS/IFREMER/IRD/University of Brest) and at the University of Southampton, and published in the 14 August 2018 edition of Nature Communications. Using a new method, the study shows that at the global level, 2018–2022 may be an even hotter period than expected based on current global warming.

Warming caused by greenhouse gas emissions is not linear: it appears to have lapsed in the early 21st century, a phenomenon known as a global warming hiatus. A new method for predicting mean temperatures, however, suggests that the next few years will likely be hotter than expected.

The system, developed by researchers at CNRS, the University of Southampton and the Royal Netherlands Meteorological Institute, does not use traditional simulation techniques. Instead, it applies a statistical method to search 20th and 21st century climate simulations made using several reference models1 to find 'analogues' of current climate conditions and deduce future possibilities. The precision and reliability of this probabilistic system proved to be at least equivalent to current methods, particularly for the purpose of simulating the global warming hiatus of the beginning of this century.

The new method predicts that mean air temperature may be abnormally high in 2018-2022 – higher than figures inferred from anthropogenic global warming alone. In particular, this is due to a low probability of intense cold events. The phenomenon is even more salient with respect to sea surface temperatures, due to a high probability of heat events, which, in the presence of certain conditions, can cause an increase in tropical storm activity. 

Once the algorithm is 'learned' (a process which takes a few minutes), predictions are obtained in a few hundredths of a second on a laptop. In comparison, supercomputers require a week using traditional simulation methods.

For the moment, the method only yields an overall average, but scientists now would like to adapt it to make regional predictions and, in addition to temperatures, estimate precipitation and drought trends.

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2018-2022 expected to be abnormally hot years


ABSTRACT: In a changing climate, there is an ever-increasing societal demand for accurate and reliable interannual predictions. Accurate and reliable interannual predictions of global temperatures are key for determining the regional climate change impacts that scale with global temperature, such as precipitation extremes, severe droughts, or intense hurricane activity, for instance. However, the chaotic nature of the climate system limits prediction accuracy on such timescales. Here we develop a novel method to predict global-mean surface air temperature and sea surface temperature, based on transfer operators, which allows, by-design, probabilistic forecasts. The prediction accuracy is equivalent to operational forecasts and its reliability is high. The post-1998 global warming hiatus is well predicted. For 2018–2022, the probabilistic forecast indicates a warmer than normal period, with respect to the forced trend. This will temporarily reinforce the long-term global warming trend. The coming warm period is associated with an increased likelihood of intense to extreme temperatures. The important numerical efficiency of the method (a few hundredths of a second on a laptop) opens the possibility for real-time probabilistic predictions carried out on personal mobile devices.

A novel probabilistic forecast system predicting anomalously warm 2018-2022 reinforcing the long-term global warming trend

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