Convection-permitting Regional Climate Model Research Articles

Improvements in the land surface configuration to better simulate seasonal snow cover in the European Alps with the CNRM-AROME (cycle 46) convection-permitting regional climate model

Abstract. Snow cover modeling remains a major challenge in climate and numerical weather prediction (NWP) models even in recent versions of high-resolution coupled surface–atmosphere (i.e., at kilometer scale) regional models. Evaluation of recent climate simulations, carried out as part of the WCRP-CORDEX Flagship Pilot Study on Convection (FPSCONV) with the CNRM-AROME convection-permitting regional climate model at 2.5 km horizontal resolution, has highlighted significant snow cover biases, severely limiting its potential in mountain regions. These biases, which are also found in AROME numerical weather prediction (NWP) model results, have multiple causes, involving atmospheric processes and their influence on input data to the land surface models in addition to deficiencies of the land surface model itself. Here we present improved configurations of the SURFEX-ISBA land surface model used in CNRM-AROME. We thoroughly evaluated these configurations on their ability to represent seasonal snow cover across the European Alps. Our evaluation was based on coupled simulations spanning the winters of 2018–2019 and 2019–2020, which were compared against remote sensing data and in situ observations. More specifically, the study tests the influence of various changes in the land surface configuration, such as the use of multi-layer soil and snow schemes, the division of the energy balance calculation by surface type within a grid cell (multiple patches), and new physiographic databases and parameter adjustments. Our findings indicate that using only more detailed individual components in the surface model did not improve the representation of snow cover due to limitations in the approach used to account for partial snow cover within a grid cell. These limitations are addressed in further configurations that highlight the importance, even at kilometer resolution, of taking into account the main subgrid surface heterogeneities and improving representations of interactions between fractional snow cover and vegetation. Ultimately, we introduce a land surface configuration, which substantially improves the representation of seasonal snow cover in the European Alps in coupled CNRM-AROME simulations. This holds promising potential for the use of such model configurations in climate simulations and numerical weather prediction both for AROME and other high-resolution climate models.

Cause and Characteristics of Changes in Mesoscale Convective Systems within a Convection-Permitting Regional Climate Model

Abstract Changes in the frequency and intensity of mesoscale convective systems (MCS) are assessed using convection-permitting regional climate model simulations. We present a novel classification method that relates MCSs to the magnitude of their large-scale forcing environments to better understand the changing nature of MCSs across different forcing environments in a possible future climate scenario. Overall, the annual frequency, intensity, and amount of precipitation associated with MCSs are projected to increase for broad portions of the eastern conterminous United States (CONUS). Furthermore, changes in the characteristics of MCSs show larger, longer-lived, and faster MCSs particularly over the Midwest and Southeast. Seasonal examination of this response reveals a robust intensification of March–May (MAM) MCSs. The higher frequency of MAM MCSs are found to occur in weakly forced synoptic environments. Increased rainfall amounts are explained by an intensification of MCSs due to changing thermodynamics and enhanced lower-tropospheric moisture transport into the central CONUS by the North Atlantic Subtropical High. Differences in mid-latitude storm tracks are favorable towards the enhanced development of MCSs associated with strong baroclinic forcing within the Midwest and Northern Plains, but are only realized in the presence of strong changes in lower-tropospheric moisture transport. Overall, these results suggest a shift in the behavior of MAM MCSs that more closely corresponds to the behavior of June–August (JJA) MCSs in the historical climate. The increased occurrence of MCSs in weakly forced environments, particularly in MAM, deserves further investigation.

Spatial Interpolation of Seasonal Precipitations Using Rain Gauge Data and Convection‐Permitting Regional Climate Model Simulations in a Complex Topographical Region

ABSTRACTIn mountainous areas, accurately estimating the long‐term climatology of seasonal precipitations is challenging due to the lack of high‐altitude rain gauges and the complexity of the topography. This study addresses these challenges by interpolating seasonal precipitation data from 3189 rain gauges across France over the 1982–2018 period, using geographical coordinates, and altitude. In this study, an additional predictor is provided from simulations of a Convection‐Permitting Regional Climate Model (CP‐RCM). The simulations are averaged to obtain seasonal precipitation climatology, which helps capture the relationship between topography and long‐term seasonal precipitation. Geostatistical and machine learning models are evaluated within a cross‐validation framework to determine the most appropriate approach to generate seasonal precipitation reference fields. Results indicate that the best model uses a machine learning approach to interpolate the ratio between long‐term seasonal precipitation from observations and CP‐RCM simulations. This method successfully reproduces both the mean and variance of observed data, and slightly outperforms the best geostatistical model. Moreover, incorporating the CP‐RCM outputs as an explanatory variable significantly improves interpolation accuracy and altitude extrapolation, especially when the rain gauge density is low. These results imply that the commonly used altitude‐precipitation relationship may be insufficient to derive seasonal precipitation fields. The CP‐RCM simulations, increasingly available worldwide, present an opportunity for improving precipitation interpolation, especially in sparse and complex topographical regions.

Spatial variability in the seasonal precipitation lapse rates in complex topographical regions – application in France

Abstract. Seasonal precipitation estimation in ungauged mountainous areas is essential for understanding and modeling a physical variable of interest in many environmental applications (hydrology, ecology, and cryospheric studies). Precipitation lapse rates (PLRs), defined as the increasing or decreasing rate of precipitation amounts with the elevation, play a decisive role in high-altitude precipitation estimation. However, the documentation of PLR in mountainous regions remains weak even though their utilization in environmental applications is frequent. This article intends to assess the spatial variability and the spatial-scale dependence of seasonal PLRs in a varied and complex topographical region. At the regional scale (10 000 km2), seven different precipitation products are compared in their ability to reproduce the altitude dependence of the annual/seasonal precipitation of 1836 stations located in France. The convection-permitting regional climate model (CP-RCM) AROME is the best in this regard, despite severe precipitation overestimation in high altitudes. The fine resolution of AROME allows for a precise assessment of the influence of altitude on winter and summer precipitation on 23 massifs at the sub-regional scale (∼ 1000 km2) and 2748 small catchments (∼ 100 km2) through linear regressions. With AROME, PLRs are often higher in winter at the catchment scale. The variability in the PLR is higher in high-altitude regions such as the French Alps, with higher PLRs at the border than inside the massifs. This study emphasizes the interest of conducting a PLR investigation at a fine scale to reduce spatial heterogeneity in the seasonal precipitation–altitude relationships.

Analysis of high-resolution convection-permitting regional climate simulations with respect to the impact of spatial resolution on simulated wind climate

In this study, climate model simulations are evaluated with regard to the wind energy resource in Germany. Since accurate determination of local wind requires a high resolution, we consider simulations of Convection Permitting Regional Climate Models (CP-RCMs) with 3km resolution. We analysed RCP8.5 scenario simulations carried out i) within the EUCP project using the RCM REMO in convection permitting mode for the Central European domain (CEU-3) and ii) within the CORDEX FPS-Convection project that provides a model ensemble of CP-RCMs for the Pan-Alpine region (ALP-3). The models are forced with RCMs at intermediate resolution which are by itself driven with General Circulation Models (GCMs) from the Coupled Model Intercomparison Project – Phase 5 (CMIP5) for the two time slices 1996-2005 (’historical’) and 2041-2050 (‘near-future’). Wind energy yield is calculated at 100 m height by using 3 MW wind turbine parameters and a height correction method. The results are compared with observations from four different met-masts at different locations. The RCMs at intermediate resolution are closer to observations than the CP-RCM REMO while the ensemble of CORDEX FPS-Convection reveals that the difference between model and observations is highly model dependent. A significant climate trend in the comparison of ’historical’ to ‘near future’ cannot be seen for Germany in both the EUCP and CORDEX FPS-Convection simulations.

Evaluation of the convection permitting regional climate model CNRM-AROME on the orographically complex island of Corsica

Meteorological processes over islands with complex orography could be better simulated by Convection Permitting Regional Climate Models (CP-RCMs) thanks to an improved representation of the orography, land–sea contrasts, the combination of coastal and orographic effects, and explicit deep convection. This paper evaluates the ability of the CP-RCM CNRM-AROME (2.5-km horizontal resolution) to simulate relevant meteorological characteristics of the Mediterranean island of Corsica for the 2000–2018 period. These hindcast simulations are compared to their driving Regional Climate Model (RCM) CNRM-ALADIN (12.5-km horizontal resolution and parameterised convection), weather stations for precipitation and wind and gridded precipitation datasets. The main benefits are found in the representation of (i) precipitation extremes resulting mainly from mesoscale convective systems affected by steep mountains during autumn and (ii) the formation of convection through thermally induced diurnal circulations and their interaction with the orography during summer. Simulations of hourly precipitation extremes, the diurnal cycle of precipitation, the distribution of precipitation intensities, the duration of precipitation events, and sea breezes are all improved in the 2.5-km simulations with respect to the RCM, confirming an added value. However, existing differences between model simulations and observations are difficult to explain as the main biases are related to the availability and quality of observations, particularly at high elevations. Overall, better results from the 2.5-km resolution, increase our confidence in CP-RCMs to investigate future climate projections for Corsica and islands with complex terrain.

The added value of simulated near-surface wind speed over the Alps from a km-scale multimodel ensemble

The advancement of computational resources has allowed researchers to run convection-permitting regional climate model (CPRCM) simulations. A pioneering effort promoting a multimodel ensemble of such simulations is the CORDEX Flagship Pilot Studies (FPS) on “Convective Phenomena over Europe and the Mediterranean” over an extended Alps region. In this study, the Distribution Added Value metric is used to determine the improvement of the representation of all available FPS hindcast simulations for the daily mean near-surface wind speed. The analysis is performed on normalized empirical probability distributions and considers station observation data as the reference. The use of a normalized metric allows for spatial comparison among the different regions (coast and inland), altitudes and seasons. This approach permits a direct assessment of the added value between the CPRCM simulations against their global driving reanalysis (ERA-Interim) and respective coarser resolution regional model counterparts. In general, the results show that CPRCMs add value to their global driving reanalysis or forcing regional model, due to better-resolved topography or through better representation of ocean-land contrasts. However, the nature and magnitude of the improvement in the wind speed representation vary depending on the model, the season, the altitude, or the region. Among seasons, the improvement is usually larger in summer than winter. CPRCMs generally display gains at low and medium-range altitudes. In addition, despite some shortcomings in comparison to ERA-Interim, which can be attributed to the assimilation of wind observations on the coast, the CPRCMs outperform the coarser regional climate models, both along the coast and inland.

Does a convection-permitting regional climate model bring new perspectives on the projection of Mediterranean floods?

Abstract. Floods are the primary natural hazard in the French Mediterranean area, causing damages and fatalities every year. These floods are triggered by heavy precipitation events (HPEs) characterized by limited temporal and spatial extents. A new generation of regional climate models at the kilometer scale have been developed, allowing an explicit representation of deep convection and improved simulations of local-scale phenomena such as HPEs. Convection-permitting regional climate models (CPMs) have been scarcely used in hydrological impact studies, and future projections of Mediterranean floods remain uncertain with regional climate models (RCMs). In this paper, we use the CNRM-AROME CPM (2.5 km) and its driving CNRM-ALADIN RCM (12 km) at the hourly timescale to simulate floods over the Gardon d'Anduze catchment located in the French Mediterranean region. Climate simulations are bias-corrected with the CDF-t method. Two hydrological models, a lumped and conceptual model (GR5H) and a process-based distributed model (CREST), forced with historical and future climate simulations from the CPM and from the RCM, have been used. The CPM model confirms its ability to better reproduce extreme hourly rainfall compared to the RCM. This added value is propagated on flood simulation with a better reproduction of flood peaks. Future projections are consistent between the hydrological models but differ between the two climate models. Using the CNRM-ALADIN RCM, the magnitude of all floods is projected to increase. With the CNRM-AROME CPM, a threshold effect is found: the magnitude of the largest floods is expected to intensify, while the magnitude of the less severe floods is expected to decrease. In addition, different flood event characteristics indicate that floods are expected to become flashier in a warmer climate, with shorter lag time between rainfall and runoff peak and a smaller contribution of base flow, regardless of the model. This study is a first step for impact studies driven by CPMs over the Mediterranean.

SINGV-RCM: the convection-permitting regional climate model for Singapore

The development of the Singapore regional climate model (SINGV-RCM) is documented in this study. The model’s skill in predicting mean and extreme rainfall over the Maritime Continent at 8 km and 2 km grid resolution is assessed using observed rainfall. The model’s sensitivity to vertical grid and convection parametrization is also evaluated. Results demonstrate the added value of downscaling to 8 and 2 km. The spatial and temporal characteristic of the diurnal rainfall is shown to be in good agreement with the observation. The model also demonstrates skill in capturing extreme rainfall. In general, the rainfall in the 8 km and 2 km simulations are quite similar, when regrided to a coarser resolution. Even then, the convection-permitting simulation at 2 km is found to add value over the 8 km simulation, particularly in capturing the higher rainfall thresholds. Mean biases over the ocean is found to be larger than that over the land, which suggests the need to further tune SINGV-RCM for long climate simulations.

Convection-Permitting Future Climate Simulations for Bulgaria under the RCP8.5 Scenario

In recent decades, climate change has become a critical global issue with far-reaching consequences for regional climates and ecosystems. While regional climate models provide valuable information, there is a growing need for high-resolution simulations to assess local impacts. This paper addresses this gap by presenting the first simulation of a 3 km convection-permitting (CP) scenario simulation for Bulgaria. The main aim of this study is to assess different precipitation indices and their future changes for Bulgaria under the Representative Concentration Pathway 8.5 (RCP8.5) scenario following the Coordinated Regional Climate Downscaling Experiment Flagship Pilot Study protocol. The simulations are evaluated against high-resolution observations. We downscale Coupled Model Intercomparison Project 5 Global Climate Model (CMIP5 GCM) data for historical (1995–2004) and future (2089–2098) periods using a regional climate model (RCM) at 15 km grid spacing and parametrized convection. We use these fields as initial and boundary conditions for convection-permitting kilometer-scale simulations. The 15 km grid spacing driving model is used as a reference to assess the added value of the kilometer-scale simulation. Additionally, the 3 km seasonal mean and projected 2 m temperature and the winter snow water equivalent are presented. The results show that the kilometer-scale simulation shows better performance of wet-hour intensity in all seasons, wet-hour frequency in the spring, fall, and winter, and extreme precipitation (99.9th percentile of all precipitation events, p99.9) in the winter and fall. The kilometer-scale simulation improves the projected precipitation distribution and modifies the signal of the precipitation frequency, intensity, and heavy precipitation change over some areas. A positive projected change in the wet-hour intensity is expected in all seasons (13.86% in spring, MAM, 17.48% in summer, JJA, 1.97% in fall, SON, and 17.43% in winter, DJF) and in the heavy precipitation in the spring (13.14%) and winter (31.19%) in the kilometer-scale experiment. The projected increase in mean winter precipitation is accompanied by a significant decrease in mean winter snowfall over lowlands (50−70%). The convection-permitting Regional Climate Model, version 4.7.1 (RegCM4.7.1) suggests an increase in winter snowfall over the highest parts of the country, but a significant increase in the 2 m temperatures there. The results of this study are encouraging and may be of interest to the community of climate scientists and users of climate data for making reliable estimates of the local impacts of future climate change.

Evaluating 3 decades of precipitation in the Upper Colorado River basin from a high-resolution regional climate model

Abstract. Convection-permitting regional climate models (RCMs) have recently become tractable for applications at multi-decadal timescales. These types of models have tremendous utility for water resource studies, but better characterization of precipitation biases is needed, particularly for water-resource-critical mountain regions, where precipitation is highly variable in space, observations are sparse, and the societal water need is great. This study examines 34 years (1987–2020) of RCM precipitation from the Weather Research and Forecasting model (WRF; v3.8.1), using the Climate Forecast System Reanalysis (CFS; CFSv2) initial and lateral boundary conditions and a 1 km × 1 km innermost grid spacing. The RCM is centered over the Upper Colorado River basin, with a focus on the high-elevation, 750 km2 East River watershed (ERW), where a variety of high-impact scientific activities are currently ongoing. Precipitation is compared against point observations (Natural Resources Conservation Service Snow Telemetry or SNOTEL), gridded climate datasets (Newman, Livneh, and PRISM), and Bayesian reconstructions of watershed mean precipitation conditioned on streamflow and high-resolution snow remote-sensing products. We find that the cool-season precipitation percent error between WRF and 23 SNOTEL gauges has a low overall bias (x^ = 0.25 %, s = 13.63 %) and that WRF has a higher percent error during the warm season (x^ = 10.37 %, s = 12.79 %). Warm-season bias manifests as a high number of low-precipitation days, though the low-resolution or SNOTEL gauges limit some of the conclusions that can be drawn. Regional comparisons between WRF precipitation accumulation and three different gridded datasets show differences on the order of ± 20 %, particularly at the highest elevations and in keeping with findings from other studies. We find that WRF agrees slightly better with the Bayesian reconstruction of precipitation in the ERW compared to the gridded precipitation datasets, particularly when changing SNOTEL densities are taken into account. The conclusions are that the RCM reasonably captures orographic precipitation in this region and demonstrates that leveraging additional hydrologic information (streamflow and snow remote-sensing data) improves the ability to characterize biases in RCM precipitation fields. Error characteristics reported in this study are essential for leveraging the RCM model outputs for studies of past and future climates and water resource applications. The methods developed in this study can be applied to other watersheds and model configurations. Hourly 1 km × 1 km precipitation and other meteorological outputs from this dataset are publicly available and suitable for a wide variety of applications.

Convection-permitting climate simulations for South America with the Met Office Unified Model

AbstractWe present the first convection-permitting regional climate model (CPRCM) simulations at 4.5 km horizontal resolution for South America at near-continental scale, including full details of the experimental setup and results from the reanalysis-driven hindcast and climate model-driven present-day simulations. We use a range of satellite and ground-based observations to evaluate the CPRCM simulations covering the period 1998–2007 comparing the CPRCM output with lower resolution regional and global climate model configurations for key regions of Brazil. We find that using the convection-permitting model at high resolution leads to large improvements in the representation of precipitation, specifically in simulating its diurnal cycle, frequency, and sub-daily intensity distribution (i.e. the proportion of heavy and light precipitation). We tentatively conclude that there are also improvements in the spatial structure of precipitation. We see higher precipitation intensity and extremes over Amazonia in the CPRCMs compared with observations, though more sub-daily observational data from meteorological stations are required to conclusively determine whether the CPRCMs add value in this regard. For annual mean precipitation and mean, maximum and minimum near surface temperatures, it is not clear that the CPRCMs add value compared with coarser-resolution models with parameterised convection. We also find large changes in the contribution to evapotranspiration from canopy evaporation compared to soil evaporation and transpiration compared with the RCM. This is likely to be related to the shift in precipitation intensity distribution of the CPRCMs compared to the RCM and its impact on the hydrological requires further investigation.

Modelling urban stormwater management changes using SWMM and convection-permitting climate simulations in cold areas

Urbanization coupled with climate change is expected to put pressure on the urban stormwater network. To predict and mitigate the effects of these trends, accurate modeling of urban stormwater changes is required at scales and resolutions meaningful to stormwater management. Although numerous studies have analyzed the effect of climate change on urban flooding risk using the event-based approach, none have incorporated the continuous modelling approach to investigate the whole spectrum of changes in an urban catchment. This study analyzes seasonal changes in future urban hydrological behavior using a mini-ensemble of six state-of-the-art climate model projections and a calibrated hydrological-hydraulic Storm Water Management Model (SWMM). The modelling results show future changes in seasonal and monthly hydrological behavior. The notable winter warming is the major driver in the future snow processes, resulting in considerably less snow days and an increase in the flow events frequency during the winter months. The modelling results also suggest an increase in the annual maximum hourly flow in all seasons, with the clearest trend modelled in winter. Monthly average runoff during the cold period is modelled to increase, while no clear trends are detected for the rest of the year. There is a clear added benefit in using convection-permitting regional climate models throughout the year. Overall, the climate change mitigation and adaption strategies in urban catchments should focus more on the whole spectrum of changes rather than only on urban pluvial flooding risk. These findings call for a transition from traditional to a more advanced stormwater management.

Investigating the representation of heatwaves from an ensemble of km-scale regional climate simulations within CORDEX-FPS convection

Heatwaves (HWs) are high-impact phenomena stressing both societies and ecosystems. Their intensity and frequency are expected to increase in a warmer climate over many regions of the world. While these impacts can be wide-ranging, they are potentially influenced by local to regional features such as topography, land cover, and urbanization. Here, we leverage recent advances in the very high-resolution modelling required to elucidate the impacts of heatwaves at these fine scales. Further, we aim to understand how the new generation of km-scale regional climate models (RCMs) modulates the representation of heatwaves over a well-known climate change hot spot. We analyze an ensemble of 15 convection-permitting regional climate model (CPRCM, ~ 2–4 km grid spacing) simulations and their driving, convection-parameterized regional climate model (RCM, ~ 12–15 km grid spacing) simulations from the CORDEX Flagship Pilot Study on Convection. The focus is on the evaluation experiments (2000–2009) and three subdomains with a range of climatic characteristics. During HWs, and generally in the summer season, CPRCMs exhibit warmer and drier conditions than their driving RCMs. Higher maximum temperatures arise due to an altered heat flux partitioning, with daily peaks up to ~ 150 W/m2 larger latent heat in RCMs compared to the CPRCMs. This is driven by a 5–25% lower soil moisture content in the CPRCMs, which is in turn related to longer dry spell length (up to double). It is challenging to ascertain whether these differences represent an improvement. However, a point-scale distribution-based maximum temperature evaluation, suggests that this CPRCMs warmer/drier tendency is likely more realistic compared to the RCMs, with ~ 70% of reference sites indicating an added value compared to the driving RCMs, increasing to 95% when only the distribution right tail is considered. Conversely, a CPRCMs slight detrimental effect is found according to the upscaled grid-to-grid approach over flat areas. Certainly, CPRCMs enhance dry conditions, with knock-on implications for summer season temperature overestimation. Whether this improved physical representation of HWs also has implications for future changes is under investigation.

Effects on early monsoon rainfall in West Africa due to recent deforestation in a convection-permitting ensemble

Abstract. Tropical deforestation can have a significant effect on climate, but research attention has been directed mostly towards Amazonian deforestation. The southern part of West Africa (a region dependent on rain-fed agriculture and vulnerable to droughts and flooding) has seen significant deforestation since the 1950s. Many previous tropical deforestation studies have used idealized and exaggerated deforestation scenarios and parameterized convection models. In this study we estimate for the first time realistic historical deforestation from the Land-Use Harmonization dataset in West Africa and simulate the impacts in a 5 d ensemble forecast in June using a convection-permitting regional climate model. We find that sensible heat flux increases at the expense of latent heat flux in most deforested regions, and rainfall increases by an average of 8.4 % over deforested pixels from 18:00–06:00 UTC, whereas changes are much less pronounced during the day. Over large areas of deforestation approx. 300 km inland (e.g. west Guinea) the roughness-length and thermally enhanced convergence during the afternoon and evening occurs over the deforested areas resulting in increases in rainfall with little impact from reduced daytime humidity. In areas of coastal deforestation (e.g. Côte d'Ivoire), increased winds drive the sea breeze convection inland, resulting in evening rainfall reductions over the deforested area but increases further inland, in line with observations. We suggest our results would not be replicated in parameterized convection models, which are known to struggle with capturing peak convective activity in the late afternoon and long-lived nocturnal rainfall and with reproducing observed surface–rainfall feedbacks.

How well does a convection-permitting regional climate model represent the reverse orographic effect of extreme hourly precipitation?

Abstract. Estimating future short-duration extreme precipitation in mountainous regions is fundamental for risk management. High-resolution convection-permitting models (CPMs) represent the state of the art for these projections, as they resolve convective processes that are key to short-duration extremes. Recent observational studies reported a decrease in the intensity of extreme hourly precipitation with elevation. This “reverse orographic effect” could be related to processes which are subgrid even for CPMs. To quantify the reliability of future projections of extreme short-duration precipitation in mountainous regions, it is thus crucial to understand to what extent CPMs can reproduce this effect. Due to the computational demands however, CPM simulations are still too short for analyzing extremes using conventional methods. We use a non-asymptotic statistical approach (Simplified Metastatistical Extreme Value: SMEV) for the analysis of extremes from short time periods, such as the ones of CPM simulations. We analyze an ERA-Interim-driven Consortium for Small-Scale Modeling (COSMO-crCLIM, convection-resolving Climate Modelling) simulation (2000–2009; 2.2 km resolution), and we use hourly precipitation from 174 rain gauges in an orographically complex area in northeastern Italy as a benchmark. We investigate the ability of the model to simulate the orographic effect on short-duration precipitation extremes, as compared to observational data. We focus on extremes as high as the 20-year return levels. While overall good agreement is reported at daily and hourly duration, the CPM tends to increasingly overestimate hourly extremes with increasing elevation, implying that the reverse orographic effect is not fully captured. These findings suggest that CPM bias-correction approaches should account for orography. SMEV's capability of estimating reliable rare extremes from short periods promises further applications on short-time-period CPM projections and model ensembles.

Evaluation of the convection-permitting regional climate model CNRM-AROME41t1 over Northwestern Europe

Since a decade, convection-permitting regional climate models (CPRCM) have emerged showing promising results, especially in improving the simulation of precipitation extremes. In this article, the CPRCM CNRM-AROME developed at the Centre National de Recherches Météorologiques (CNRM) since a few years is described and evaluated using a 2.5-km 19-year long hindcast simulation over a large northwestern European domain using different observations through an added-value analysis in which a comparison with its driving 12-km RCM CNRM-ALADIN is performed. The evaluation is challenging due to the lack of high-quality observations at both high temporal and spatial resolutions. Thus, a high spatio-temporal observed gridded precipitation dataset was built from the collection of seven national datasets that helped the identification of added value in CNRM-AROME. The evaluation is based on a series of standard climatic features that include long-term means and mean annual cycles of precipitation and near-surface temperature where CNRM-AROME shows little improvements compared to CNRM-ALADIN. Additional indicators such as the summer diurnal cycle and indices of extreme precipitation show, on the contrary, a more realistic behaviour of the CNRM-AROME model. Moreover, the analysis of snow cover shows a clear added-value in the CNRM-AROME simulation, principally due to the improved description of the orography with the CPRCM high resolution. Additional analyses include the evaluation of incoming shortwave radiation, and cloud cover using satellite estimates. Overall, despite some systematic biases, the evaluation indicates that CNRM-AROME is a suitable CPRCM that is superior in many aspects to the RCM CNRM-ALADIN.

What added value of CNRM-AROME convection-permitting regional climate model compared to CNRM-ALADIN regional climate model for urban climate studies ? Evaluation over Paris area (France)

The convection-permitting regional climate model CNRM-AROME was applied on a spatial domain restricted to the northern half of France for analyzing its performances in simulating the urban climate of Paris region, and its potential added value compared to the regional climate model CNRM-ALADIN. In addition to its fine horizontal resolution (2.5 km compared to 12.5 km for CNRM-ALADIN), CNRM-AROME has the advantage of integrating the urban canopy model TEB into its land-surface modeling system. A hindcast simulation was performed for the past period 2000–2017, following an evaluation configuration for which CNRM-AROME was driven by CNRM-ALADIN, driven itself by the ERA-Interim reanalyses. Long-term gridded observations with kilometric resolution allowed a fine spatial scale evaluation of the atmospheric variables simulated by both models. They showed in particular a significant overestimation of spring precipitation, but an improvement of summer precipitation in CNRM-AROME compared to CNRM-ALADIN. Above all, thanks to its horizontal resolution and the use of a dedicated urban model, CNRM-AROME was shown to offer significant added value for the simulation of urban heat islands, for the mapping of heat-warming areas, and for representing the effects of the city on precipitation. It is a promising tool to diagnose climatic and impact indicators at the city scale, and their evolution in a changing climate.

The added value of km-scale simulations to describe temperature over complex orography: the CORDEX FPS-Convection multi-model ensemble runs over the Alps

The increase in computational resources has enabled the emergence of multi-model ensembles of convection-permitting regional climate model (CPRCM) simulations at very high horizontal resolutions. An example is the CORDEX Flagship Pilot Study on “Convective phenomena at high resolution over Europe and the Mediterranean”, a set of kilometre-scale simulations over an extended Alpine domain. This first-of-its-kind multi-model ensemble, forced by the ERA-Interim reanalysis, can be considered a benchmark dataset. This study uses a recently proposed metric to determine the added value of all the available Flagship Pilot Study hindcast kilometre-scale simulations for maximum and minimum temperature. The analysis is performed using state-of-the-art gridded and station observations as ground truth. This approach directly assesses the added value between the high-resolution CPRCMs against their driving global simulations and coarser resolution RCM counterparts. Overall, models display some modest gains, but also considerable shortcomings are exhibited. In part, these deficiencies can be attributed to the assimilation of temperature observations into ERA-Interim. Although the gains for the use of kilometre-scale resolution for temperature are limited, the improvement of the spatial representation of local atmospheric circulations and land–atmosphere interactions can ultimately lead to gains, particularly in coastal areas.

Evaluation of the Urban Heat Island of 12 cities of France in a high-resolution regional climate model simulation

The evolution with climate change of local urban climate could exacerbate the environmental and health issues for populations. The CNRM-AROME Convection-Permitting Regional Climate Model (CP-RCM) was coupled to the Town Energy Balance (TEB) urban canopy model at 2.5-km of horizontal resolution to explicitly account for the physical urban-climate processes in interaction with the regional climate conditions. A hindcast simulation over 2000-2018 was analyzed to investigate the potential of this new high-resolution configuration for urban-oriented studies. The simulated urban climate of twelve French cities located in contrasted geographic and climatic contexts was evaluated against observations on a climatological basis. The study of the local atmospheric conditions for each city shows that CNRM-AROME systematically overestimates the incoming solar radiation and precipitation (except in summer), explaining partially the warm(cold) biases in minimum(maximum) daily temperatures. Nevertheless, CNRM-AROME reproduces the UHIN seasonal variability and intensity over most cities, and the regional disparities related to the climatic zones to which the cities belong. The urban climate of coastal cities remains challenging to simulate due to complex local forcings and excessive warm and dry conditions simulated in summer. Overall, the coupling of CNRM-AROME with TEB stands as a relevant tool for studying city-scale weather conditions.