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Convection-permitting models (CPMs) are receiving growing scientific interest for their capability to accurately simulate extreme weather events at a kilometer-scale spatial resolution, offering valuable information for local climate change adaptation. This study employs both qualitative and quantitative bibliometric analysis techniques to examine research trends in CPM, utilizing data from 3508 articles published between 2000 and 2023. The annual number of publications exhibits a linear increase, rising from fewer than 50 in 2000 to over 250 after 2020, with the majority of research originating from the US, China, the UK, and Germany. The most productive institutes include the National Oceanic Atmospheric Administration (NOAA) and the National Center for Atmospheric Research (NCAR) in the US, each contributing over 10% of total publications. Title and abstract terms in publications related to keywords such as "scenario", "climate simulation", etc., dominate publications from 2018 to 2023, coinciding with advances in computing power. Notably, terms associated with CPM physical processes received the highest citations from 2000 to 2023, underscoring the importance of such these research topics. Given the computational expense of running CPMs and the increasing demand for future predictions using CPMs, novel methods for generating long-term simulations are imperative. [ABSTRACT FROM AUTHOR]

In the European Alps, surface snow cover is of high relevance and a major factor for environmental, ecological and economical systems. The provision of accurate and timely climatological information on the spatio-temporal distribution of Alpine snow cover is hence an important yet challenging task of modern climate services. To assess the quality and the reliability of existing snow cover products in this region, we here compare daily snow cover datasets over Switzerland from the low-resolution ERA5 reanalysis, two intermediate resolution reanalyses (ERA5-Land and COSMO-REA6) and a high-resolution AVHRR-derived remote sensing product with three high-resolution 1 km offline snow models for the past 30 to 60 years. We focus on the parameters snow water equivalent (SWE) and daily snow coverage (snow/no snow). In the challenging Alpine terrain, all datasets are able to broadly represent the mean and the seasonal cycle of SWE and the number of snow days. They also agree on the direction of the trends. Decreasing mean SWE and snow day trends are found for the whole of Switzerland and all subregions in the period 1982–2019. Regarding interannual variability and trends, ERA5, ERA5-Land and COSMO-REA6 seem to perform reasonably well in most regions although the absolute and relative biases can be considerable. Especially ERA5-Land strongly overestimates SWE at high elevations. The biases are often larger in the southern Alps than in and north of the Alps. Most datasets, including the high-resolution ones, have problems correctly representing small SWE values at low elevations. The results indicate that in order to estimate absolute SWE or snow days with a reasonable accuracy, or to conduct a detailed study of the elevation dependence of snow, a km‑scale model that assimilates snow measurements is highly preferable. MeteoSwiss and the WSL Institute for Snow and Avalanche Research SLF are currently setting up a 1 km daily operational climatological product covering the period from 1961 to today.

This study presents a comprehensive assessment of a dynamical downscaling of ERA5 Reanalysis recently performed over Italy through the COSMO-CLM model at a convection-permitting scale (0.02°) over the period 1989–2020. Results are analysed against several independent observational datasets and reanalysis products. The capability of the downscaling to realistically represent the climatology for 2 m temperature and precipitation is analysed over the whole peninsula and subdomains. Hourly precipitation patterns, orography effects, and urban climate dynamics are also investigated, highlighting the weaknesses and strengths of the convection-permitting model. In particular, gains in performances are achieved in mountainous areas where the climate characteristics are correctly represented, as are the hourly precipitation characteristics. Losses in performances occur in coastal and flat areas of the Italian peninsula, where the convection-permitting model performance does not seem to be satisfactory, as opposed to complex orographic areas. The adopted urban parameterisation is demonstrated to simulate heat detection for two Italian cities: Rome and Milan. Finally, a subset of extreme climate indicators is evaluated, finding: (i) a region-dependent response, (ii) a notable performance of the convection-permitting model over mountainous areas and (iii) discrepancies in the South, Central and Insular subdomains. Climate indicators detect extreme events at a detailed scale, becoming an important tool for turning climate data into information. [ABSTRACT FROM AUTHOR]

Generations of climate models exhibit biases in their representation of North Atlantic storm tracks, which tend to be too far near the equator and too zonal. Additionally, models have difficulties simulating explosive cyclone growth. These biases are one of the reasons for the uncertainties in projections of future climate over Europe, and the underlying causes have yet to be determined. All three biases are shown to be related, and diabatic processes are pointed to as a likely cause. To demonstrate this, two hemispherically symmetric storm tracks forming downstream of an idealized sea surface temperature (SST) front on an aquaplanet are examined using the seamless weather and climate prediction model ICOsahedral Non‐hydrostatic and its grid refinement capabilities. The analyzed perpetual boreal winter has a global grid spacing of 20 km, two bi‐directionally interacting grid nests over the Northern Hemisphere that refine the grid to 10‐km spacing over much of the stormtrack and further to 5‐km spacing near the SST front. In contrast, no grid refinement is performed for the Southern Hemisphere. Feature‐based cyclone tracking shows that the poleward propagation in the NH is enhanced, so the high‐resolution storm track is less equatorward and less zonal; explosive deepening rates are more frequent and precipitation rates are amplified. The implication is that resolving diabatic processes on the storm scale improves all three intersecting biases in the representation of storm tracks. While new challenges arise at cloud resolving scales, much improvement for the representation of storm tracks will be gained once climate models resolve the meso‐γ scale. Plain Language Summary: Most people in the mid‐latitudes will experience climate change through changes in their daily weather. Much of the daily weather variability is determined by the propagation of extratropical low pressure systems. The direction of propagation of these systems dictates regional precipitation patterns, and an accurate representation of the track is important to reduce uncertainties in future projections. However, climate models simulate tracks that are too zonal (i.e., east–west), too close to the equator and too weak. Using an idealized simulation, this study shows that individual tracks will propagate more poleward, intensification rates will increase and the tracks become less zonal at storm‐resolving model resolution, helping to reduce this well‐known circulation bias in climate models. Key Points: Generations of climate models simulate storm tracks too zonal, too equatorward and insufficiently represent explosive cyclone growthA 10‐year simulation with a local 5‐km grid nest suggests that all three biases are interrelated, because poleward storm propagation is inherently connected to intensificationEvidence is presented that diabatic processes are an important contributor to all three biases and convection‐resolving simulations have the potential to improve the storm track representation [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]