2018 to 2022 is predicted to be an anomalously warm period with an increased likelihood of extreme temperatures, according to a probabilistic forecast system reported in Nature Communications. The study presents a statistical model that produces predictions of global mean surface air temperature in a few hundredths of a second on a laptop, opening up the possibility of real-time probabilistic forecasting on personal devices.
Changes in global mean surface temperature can be attributed to external forcing (such as greenhouse gas emissions or aerosols, which follow particular socioeconomic scenarios) and to natural variability in the system, which is harder to predict. Therefore, improvements in predicting natural variability are required for more accurate interannual climate forecasts.
Florian Sevellec and Sybren Drijfhout develop a statistical approach based on transfer operators - an established statistical analysis method that rationalizes the chaotic behaviour of a system - that captures natural variability. This system provides reliable probabilistic predictions of global mean surface temperature and sea-surface temperature. A forecast for 2018-2022 indicates that warming owing to natural variability will temporarily reinforce the long-term global warming trend, leading to an increase in the likelihood of temperature extremes.
Although the system only forecasts one metric at a time, it can be adjusted to predict other measures, such as precipitation, and to focus on regional scales. In addition, the system can be run on a laptop, which could broaden access to climate forecasts to a wider scientific community.