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 $$\sim $$∼ 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 ( $$\sim $$∼ 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 $$\sim \,$$∼ −40% at 12 km to $$\sim \,$$∼ −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.

The seasonal dependence of Weather Research and Forecasting (WRF) model surface temperature biases and sensitivity to planetary boundary layer (PBL) schemes are jointly explored. For this purpose, the year 2001 was simulated using three different PBL schemes in a domain covering all Europe. The simulations were compared with gridded observations, upper‐air data and high‐frequency station data. Seasonal and daily cycles were analysed, aimed at providing a link between long‐term biases and restricted case studies. The results show that the model mean bias significantly depends on the season, being warm in winter and cold in summer. The winter warm bias is related to misrepresented cold extremes, while a systematic cold bias dominates the whole temperature range in summer. Regarding PBL schemes, an overall underestimation of the entrainment is found, with the non‐local Yonsei University scheme producing systematically warmer temperatures. It is shown that the opposite seasonal biases and systematic behaviour of the PBL schemes during the year lead to a different best‐performing scheme in winter and summer. Moreover, the best‐performing PBL scheme in an average sense is a result of the compensation of errors. The average summer results can be partially explained by a detailed case study. It is concluded that short‐term studies should be used with caution to decide on the parametrizations to be used in long‐term simulations. Copyright © 2012 Royal Meteorological Society

Including the impacts of climate change in decision making and planning processes is a challenge facing many regional governments including the New South Wales (NSW) and Australian Capital Territory (ACT) governments in Australia. NARCliM (NSW/ACT Regional Climate Modelling project) is a regional climate modelling project that aims to provide a comprehensive and consistent set of climate projections that can be used by all relevant government departments when considering climate change. To maximise end user engagement and ensure outputs are relevant to the planning process, a series of stakeholder workshops were run to define key aspects of the model experiment including spatial resolution, time slices, and output variables. As with all such experiments, practical considerations limit the number of ensemble members that can be simulated such that choices must be made concerning which global climate models (GCMs) to downscale from, and which regional climate models (RCMs) to downscale with. Here a methodology for making these choices is proposed that aims to sample the uncertainty in both GCM and RCM ensembles, as well as spanning the range of future climate projections present in the GCM ensemble. The RCM selection process uses performance evaluation metrics to eliminate poor performing models from consideration, followed by explicit consideration of model independence in order to retain as much information as possible in a small model subset. In addition to these two steps the GCM selection process also considers the future change in temperature and precipitation projected by each GCM. The final GCM selection is based on a subjective consideration of the GCM independence and future change. The created ensemble provides a more robust view of future regional climate changes. Future research is required to determine objective criteria that could replace the subjective aspects of the selection process.

The Andes is the most biodiverse region across the globe. In addition, some of the largest urban areas in South America are located within this region. Therefore, ecosystems and human population are affected by hydroclimate changes reported at global, regional and local scales. This paper summarizes progress of knowledge about long-term trends observed during the last two millennia over the entire Andes, with more detail for the period since the second half of the 20th century, and presents a synthesis of climate change projections by the end of the 21st century. In particular, this paper focuses on temperature, precipitation and surface runoff in the Andes. Changes in the Andean cryosphere are not included here since this particular topic is discussed in other paper in this Frontiers special issue, and elsewhere (e.g. IPCC, 2019b). While previous works have reviewed the hydroclimate of South America and particular sectors (i.e., Amazon and La Plata basins, the Altiplano, Northern South America, etc.) this review includes for the first time the entire Andes region, considering all latitudinal ranges: tropical (North of 27°S), subtropical (27°S−37°S) and extratropical (South of 37°S). This paper provides a comprehensive view of past and recent changes, as well as available climate change projections, over the entire Andean range. From this review, the main knowledge gaps are highlighted and urgent research necessities in order to provide more mechanistic understanding of hydroclimate changes in the Andes and more confident projections of its possible changes in association with global climate change.

Tropical-like storms on the Mediterranean Sea are occasionally observed on satellite images, often with a clear eye surrounded by an axysimmetric cloud structure. These storms sometimes attain hurricane intensity and can severely affect coastal lands. A deep, cut-off, cold-core low is usually observed at mid-upper tropospheric levels in association with the development of these tropical-like systems. In this study we attempt to apply some tools previously used in studies of tropical hurricanes to characterise the environments in which seven known Mediterranean events developed. In particular, an axisymmetric, nonhydrostatic, cloud resolving model is applied to simulate the tropical-like storm genesis and evolution. Results are compared to surface observations when landfall occurred and with satellite microwave derived wind speed measurements over the sea. Finally, sensitivities of the numerical simulations to different factors (e.g. sea surface temperature, vertical humidity profile and size of the initial precursor of the storm) are examined.