Predictability of weather and climate

Abstract:

Existing weather models are known to have poor skill at forecasting rainfall over East Africa. Improved forecasts could reduce the effects of extreme weather events and provide significant socioeconomic benefits to the region. We present a novel machine learning based method to improve precipitation forecasts in East Africa, using postprocessing based on a conditional generative adversarial network (cGAN). This addresses the challenge of realistically representing tropical rainfall, where convection dominates and is poorly simulated in conventional global forecast models. We postprocess hourly forecasts made by the European Centre for Medium-Range Weather Forecasts Integrated Forecast System at 6-18h lead times, at 0.1° resolution. We combine the cGAN predictions with a novel neighbourhood version of quantile mapping, to integrate the strengths of machine learning and conventional postprocessing. Our results indicate that the cGAN substantially improves the diurnal cycle of rainfall, and improves predictions up to the 99.9th percentile (∼ 10mm/hr). This improvement extends to the 2018 March–May season, which had extremely high rainfall, indicating that the approach has some ability to generalise to more extreme conditions. We explore the potential for the cGAN to produce probabilistic forecasts and find that the spread of this ensemble broadly reflects the predictability of the observations, but is also characterised by a mixture of under- and overdispersion. Overall our results demonstrate how the strengths of machine learning and conventional postprocessing methods can be combined, and illuminate what benefits ma38 chine learning approaches can bring to this region.

Abstract:

Significant changes have occurred during the last few decades across the North Atlantic climate system, including in the atmosphere, ocean, and cryosphere. These large-scale changes play a vital role in shaping regional climate and extreme weather events across the UK and Western Europe. This review synthesizes the characteristics of observed large-scale changes in North Atlantic atmospheric and oceanic circulations during past decades, identifies the drivers and physical processes responsible for these changes, outlines projected changes due to anthropogenic warming, and discusses the predictability of these circulations. On multi-decadal time scales, internal variability, anthropogenic forcings (especially greenhouse gases), and natural forcings (such as solar variability and volcanic eruptions) are identified as key contributors to large-scale variability in North Atlantic atmospheric and oceanic circulations. However, there remain many uncertainties regarding the detailed characteristics of these various influences, and in some cases their relative importance. We therefore conclude that a better understanding of these drivers, and more accurate quantification of their relative roles, are crucial for more reliable decadal predictions and projections of regional climate for the North Atlantic and Europe.<h4>Supplementary information</h4>The online version contains supplementary material available at 10.1007/s00382-025-07591-1.