The signal-to-noise paradox in climate forecasts: revisiting our understanding and identifying future priorities

Bulletin of the American Meteorological Society American Meteorological Society (2024)


Antje Weisheimer, Laura Baker, Jochen Bröcker, Chaim Garfinkel, Steven Hardiman, Dan Hodson, Tim Palmer, J Robson, Adam Scaife, James Screen, T Shepherd, D Smith, R Sutton

Event attribution of a midlatitude windstorm using ensemble weather forecasts

Environmental Research: Climate IOP Publishing 3:3 (2024) 035001-035001


Shirin Ermis, Nicholas J Leach, Fraser C Lott, Sarah N Sparrow, Antje Weisheimer


Abstract The widespread destruction incurred by midlatitude storms every year makes it an imperative to study how storms change with climate. The impact of climate change on midlatitude windstorms, however, is hard to evaluate due to the small signals in variables such as wind speed, as well as the high resolutions required to represent the dynamic processes in the storms. Here, we assess how storm Eunice, which hit the UK in February 2022, was impacted by anthropogenic climate change using the ECMWF ensemble prediction system. This system was demonstrably able to predict the storm, significantly increasing our confidence in its ability to model the key physical processes and their response to climate change. Using modified greenhouse gas concentrations and changed initial conditions for ocean temperatures, we create two counterfactual scenarios of storm Eunice in addition to the forecast for the current climate. We compare the intensity and severity of the storm between the pre-industrial, current, and future climates. Our results robustly indicate that Eunice has become more intense with climate change and similar storms will continue to intensify with further anthropogenic forcing. These results are consistent across forecast lead times, increasing our confidence in them. Analysis of storm composites shows that this process is caused by increased vorticity production through increased humidity in the warm conveyor belt of the storm. This is consistent with previous studies on extreme windstorms. Our approach of combining forecasts at different lead times for event attribution enables combining event specificity and a focus on dynamic changes with the assessment of changing risks from windstorms. Further work is needed to develop methods to adjust the initial conditions of the atmosphere for the use in attribution studies using weather forecasts but we show that this approach is viable for reliable and fast attribution systems.

North-West Europe hottest days are warming twice as fast as mean summer days

Geophysical Research Letters American Geophysical Union 50:10 (2023) e2023GL102757


Europe has seen a rapid increase in the frequency and intensity of hot extremes in recent decades. In this study it is shown, using ERA5 reanalysis data 1960–2021, that the hottest summer days in North-West Europe are warming approximately twice as fast as mean summer days. Moreover, this pattern stands out as relatively unusual across the Northern Hemisphere. It is also shown that comprehensive climate models fail to capture this difference in trends. A hypothesis is suggested to explain the differential rate of warming between the mean and hottest days, namely that the hottest days are often linked to warm advection from Iberia and North Africa, areas that are warming faster than North-West Europe. This hypothesis can account for about 25% of the difference between ERA5 and a climate model ensemble and hence further research is needed to understand the drivers of the differing trends in mean and extreme temperature.

The link between North Atlantic tropical cyclones and ENSO in seasonal forecasts

Atmospheric Science Letters Wiley 25:1 (2023) e1190


Robert Doane-Solomon, Daniel J Befort, Joanne Camp, Kevin Hodges, Antje Weisheimer


This study assesses the ability of six European seasonal forecast models to simulate the observed teleconnection between ENSO and tropical cyclones (TCs) over the North Atlantic. While the models generally capture the basin-wide observed link, its magnitude is overestimated in all forecast models compared to reanalysis. Furthermore, the ENSO-TC relationship in the Caribbean is poorly simulated. It is shown that incorrect forecasting of wind shear appears to affect the representation of the teleconnection in some models, however it is not a completely sufficient explanation for the overestimation of the link.

A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts



Lucy Harris, Andrew TT McRae, Matthew Chantry, Peter D Dueben, Tim N Palmer