Nikolina Ban, Cécile Caillaud, Erika Coppola, Emanuela Pichelli, Stefan Sobolowski, Marianna Adinolfi, Bodo Ahrens, Antoinette Alias, Ivonne Anders, Sophie Bastin, Danijel Belušić, Ségolène Berthou, Erwan Brisson, Rita M. Cardoso, Steven Chan, Ole Bøssing Christensen, Jesús Fernández, Lluís Fita, Thomas Frisius, Goran Gašparac, Filippo Giorgi, Klaus Goergen, Jan Erik Haugen, Øivind Hodnebrog, Stergios Kartsios, Eleni Katragkou, Elizabeth J. Kendon, Klaus Keuler, Alvaro Lavin-Gullon, Geert Lenderink, David Leutwyler, Torge Lorenz, Douglas Maraun, Paola Mercogliano, Josipa Milovac, Hans-Juergen Panitz, Mario Raffa, Armelle Reca Remedio, Christoph Schär, Pedro M. M. Soares, Lidija Srnec, Birthe Marie Steensen, Paolo Stocchi, Merja H. Tölle, Heimo Truhetz, Jesus Vergara-Temprado, Hylke de Vries, Kirsten Warrach-Sagi, Volker Wulfmeyer & Marjanne Zander
If we are to limit the impacts of climate change, it is essential to predict its effects as accurately as possible. Extreme rainfall is one of the most severe weather events that can be impacted by climate change, and improved supercomputers are now allowing us to run higher resolution climate models to predict changes in extreme events more accurately. This study presents the first multi-model ensemble of decade-long regional climate models run at kilometre scale, involving research teams from across Europe. These simulations were compared with lower resolution models and with observed values, showing that high resolution gives a significant improvement in model performance. Uncertainty ranges in the simulations were also halved, increasing the confidence we can have in these results. Policymakers rely on accurate climate information to formulate effective measures to adapt to and mitigate the impact of climate change, and this study presents a useful method to improve our predictions of extreme rainfall.
Regional Climate Models (RCMs) are high-resolution models of the Earth’s climate able to improve the simulation of extreme weather events. At their highest resolutions they are capable of simulating atmospheric convection, a key process in many extreme weather events, rather than using pre-determined convection values. They are widely used in weather forecasting, but they require large supercomputing resources, limiting their use in longer-term climate modelling. Improved computer power, however, has now made their use in climate prediction more viable. This study presents the first multi-model ensemble of decade-long RCM simulations at convection-resolving resolution. These simulations are compared with those from lower resolution RCMs to see if higher resolution confers benefits in predicting extreme rainfall, an important climate impact. Improving these predictions helps people and policymakers formulate climate adaptation and mitigation measures with the best available information.
The team found that their high-resolution simulations were better in many cases than the lower resolution ones. This was especially true in summer, when the low-resolution model overestimated the frequency and underestimated the intensity of daily and hourly rainfall. The benefit of a higher resolution was most pronounced for heavy rainfall events. On average, the low-resolution models underestimated summer heavy rainfall per hour by ~40%. The high-resolution models only underestimated this rainfall by ~3%. The uncertainty ranges in the simulations were also almost halved at a high resolution, increasing our confidence in their results. Summer daily rainfall cycles, the frequency of wet hours and heavy rainfall were specifically assessed over Switzerland, France and Italy. In all three, the high-resolution simulations presented improvements over their lower resolution counterparts, however a large spread still existed in the high-resolution ensemble. Overall, the authors state that high-resolution RCMs bring significant improvements in representing extreme rainfall over lower resolution models, helping us understand these damaging events and their trends.
This paper utilised six different RCMs run multiple times for a total of 23 high-resolution simulations performed by 22 different European research groups. These were compared with 22 lower resolution simulations. The high-resolution simulations were run with a 2.2–3 km grid size and simulated their own convection, while the low-resolution simulations had a 12–25 km grid size and convection values provided by convection approximations (known as parameterizations). These simulations predicted rainfall dynamics from 2000-2009, using climate data from the ERA-Interim Reanalysis as a starting point. The simulated rainfall during this period was compared with several observed rainfall datasets using several metrics, assessing how well the models had simulated real events.
These results provide a pathway to improving our predictions of extreme rainfall events using high-resolution RCMs. Improving our predictions in this way will allow better-informed decisions to be made by policymakers and communities to adapt to and mitigate the impact of this important facet of climate change.
Here we present the first multi-model ensemble of regional climate simulations at kilometer-scale horizontal grid spacing over a decade long period. A total of 23 simulations run with a horizontal grid spacing of 3 km, driven by ERA-Interim reanalysis, and performed by 22 European research groups are analysed. Six different regional climate models (RCMs) are represented in the ensemble. The simulations are compared against available high-resolution precipitation observations and coarse resolution ( 12 km) RCMs with parameterized convection. The model simulations and observations are compared with respect to mean precipitation, precipitation intensity and frequency, and heavy precipitation on daily and hourly timescales in different seasons. The results show that kilometer-scale models produce a more realistic representation of precipitation than the coarse resolution RCMs. The most significant improvements are found for heavy precipitation and precipitation frequency on both daily and hourly time scales in the summer season. In general, kilometer-scale models tend to produce more intense precipitation and reduced wet-hour frequency compared to coarse resolution models. On average, the multi-model mean shows a reduction of bias from −40% at 12 km to −3% at 3 km for heavy hourly precipitation in summer. Furthermore, the uncertainty ranges i.e. the variability between the models for wet hour frequency is reduced by half with the use of kilometer-scale models. Although differences between the model simulations at the kilometer-scale and observations still exist, it is evident that these simulations are superior to the coarse-resolution RCM simulations in the representing precipitation in the present-day climate, and thus offer a promising way forward for investigations of climate and climate change at local to regional scales.