This paper presents a new version of HCLIM, a regional climate modelling system based on the ALADIN–HIRLAM numerical weather prediction (NWP) system. HCLIM uses atmospheric physics packages from three NWP model configurations, HARMONIE–AROME, ALARO and ALADIN, which are designed for use at different horizontal resolutions. The main focus of HCLIM is convection-permitting climate modelling, i.e. developing the climate version of HARMONIE–AROME. In HCLIM, the ALADIN and ALARO configurations are used for coarser resolutions at which convection needs to be parameterized. Here we describe the structure and development of the current recommended HCLIM version, cycle 38. We also present some aspects of the model performance.
HCLIM38 is a new system for regional climate modelling, and it is being used in a number of national and international projects over different domains and climates ranging from equatorial to polar regions. Our initial evaluation indicates that HCLIM38 is applicable in different conditions and provides satisfactory results without additional region-specific tuning.
HCLIM is developed by a consortium of national meteorological institutes in close collaboration with the ALADIN–HIRLAM NWP model development. While the current HCLIM cycle has considerable differences in model setup compared to the NWP version (primarily in the description of the surface), it is planned for the next cycle release that the two versions will use a very similar setup. This will ensure a feasible and timely climate model development as well as updates in the future and provide an evaluation of long-term model biases to both NWP and climate model developers.
Regional climate models (RCMs) are currently used at a variety of spatial scales and model grid resolutions. Since the main motivation of using RCMs is to add value compared to global climate models (GCMs) by downscaling over a limited-area domain, the resolution of RCMs is several times higher than that of GCMs
HCLIM cycle 38 (HCLIM38) has been developed and maintained by a subset of national meteorological institutes from the HIRLAM consortium: AEMET (Spain), DMI (Denmark), FMI (Finland), KNMI (the Netherlands), MET Norway and SMHI (Sweden). HCLIM38 is the new recommended version with considerable changes and improvements compared to the older versions. It is currently being used in a number of projects, some of which are large collaborative activities (e.g. the H2020 European Climate Prediction system – EUCP,
The purpose of this paper is to describe the HCLIM38 modelling system and the available model configurations, as well as to provide initial insight into some aspects of their performance. HCLIM38 is closely related to the documented ALADIN–HIRLAM NWP modelling system, and therefore this paper focuses on the features that distinguish the climate modelling system from its NWP counterpart. Extensive evaluations of the different HCLIM38 model configurations and for different regions will be presented in separate studies.
HCLIM is a regional climate model based on the NWP model configuration and scripting system called HARMONIE–AROME, which is a part of the ALADIN–HIRLAM (see Table
List of main acronyms related to the HCLIM system.
The HCLIM climate model development is based on the HARMONIE system. The HARMONIE–AROME model configuration is designed for convection-permitting scales and is used with nonhydrostatic dynamics, which is the primary focus of HCLIM development. The model configurations ALADIN and ALARO are also used in HCLIM applications, typically for coarser resolutions with hydrostatic dynamics. Since HCLIM includes these three different model configurations, it is necessary to specify which configuration is used for a given application. Together with specifying the cycle used (cycle 38 in this paper), this results in the final terminology that is used in these projects: HCLIM38-AROME, HCLIM38-ALADIN or HCLIM38-ALARO.
As seen above, HCLIM versions are called cycles to remain consistent with the NWP model configurations. The cycle numbering of model versions is inherited from the ECMWF, who are the first in the chain of model releases
The current and historical versions of the code are archived using Apache Subversion (SVN;
Unlike the majority of limited-area models, a common characteristic of all model configurations in the HCLIM system is that they use a bi-spectral representation for most prognostic variables, with semi-implicit time integration and a semi-Lagrangian advection scheme. The details of the dynamics are described in
Intended horizontal grid resolutions for the three model configurations available in HCLIM38 (based on
SURFEX is an externalized surface modelling system that parameterizes all components of the surface
The NWP setup of HARMONIE–AROME
SURFEX parameterizations used in HCLIM38.
The land cover and soil properties are obtained from the ECOCLIMAP version 2.2 database at 1 km resolution
ALADIN is the default choice in HCLIM38 for simulations with grid spacing close to or larger than 10 km. It is the limited-area version of the global model ARPEGE, from which it inherits all dynamics and physics options. The version of ALADIN available in HCLIM38 corresponds to the one used in NWP, with the dynamics and physics options listed in Table
ALARO has been designed to also operate in the convection grey zone (Fig.
Parameterizations and dynamics of the three model configurations as used in HCLIM38.
HCLIM38-AROME is a nonhydrostatic CPRCM based on the HARMONIE–AROME NWP model configuration (Table
There is no deep convection parameterization in AROME, so it can only be used at convection-permitting resolutions, which are generally considered grid spacings smaller than 4 km. This implies that for downscaling low-resolution GCMs, it is currently not possible to use the same model configuration at intermediate scales of O(10 km) and convection-permitting scales of O(1 km). As described above, the intermediate scales in the current setup are typically simulated by HCLIM38-ALADIN. This is the preferred option because the two model configurations share the same surface model. In this case the soil state in HCLIM38-AROME is initialized in a consistent way, the only difference being the resolution change. This could help decrease the soil spin-up time, which is typically 1 year, provided that the precipitation climatology is similar between the two model configurations. However, HCLIM38-AROME can also be used with other climate models at the lateral boundaries.
HCLIM38-AROME uses a shallow convection parameterization based on the eddy diffusivity mass-flux framework (EDMFm;
Since the names and model configurations are similar for HCLIM and Météo-France–CNRM climate models (CNRM-ALADIN and CNRM-AROME), we briefly describe some of the main differences between the latest versions of the HCLIM and CNRM models (see Table
CNRM-ALADIN has been used in climate mode for more than 10 years
The CNRM-AROME41t1 model is currently used for climate research
The version of the latest operational HCLIM was cycle 36, described thoroughly by
In HCLIM38, ALADIN is the default model configuration that should be used for coarser-resolution applications and as the intermediate model when the target horizontal grid size is on the kilometre scale or below. In the previous cycles the default configuration was ALARO. The other main difference is the update of the surface modelling platform going from SURFEX v5 to a blend of SURFEX v7 and v8 in order to improve and enable more accurate land-related processes. The main improvements in SURFEX processes in cycle 38 are summarized in the following. Similarly to the differences from the current NWP configuration (see Sect.
The three configurations of HCLIM38 have been used over several different regions and climates. Since HCLIM38-ALADIN is a limited-area version of the global model ARPEGE, the expectation is that it can be used in principle for any region on Earth. Consequently, the model is not tuned for specific regions. HCLIM38-ALARO has mostly been used in high latitudes where it performs well. HCLIM38-AROME has been successfully used on domains ranging from the tropics and different mid-latitude regions to the Arctic, indicating that it can be used for various climates without additional modifications. Here we illustrate the capability and performance of HCLIM38 with a number of selected examples for different domains and model configurations. More in-depth evaluation studies are left for separate papers for each of the domains analysed below as well as for other domains
Difference between RCMs and E-OBS of near-surface temperature (
Here, HCLIM38 is compared to other state-of-the-art RCMs over Europe
Due to its complex topography and the exposure of the western coast to large amounts of moisture transported over the North Atlantic, Norway is a challenging area for which to provide accurate climate model simulations and constitutes an ideal testing environment. To evaluate the performance of the HCLIM38-AROME model in this region at convection-permitting scales, it was run at 2.5 km for an area covering Norway, parts of Finland, Sweden and Russia for the time period 2004–2016
Annual temperature differences between HCLIM38-AROME and seNorge for the time period 2004–2015.
For the evaluation over Norway, we use the gridded observation dataset seNorge v2.1 for precipitation
Note that gridded observation datasets in remote regions of Norway should be considered with caution. For certain regions of Norway it may be difficult to judge the quality of the seNorge data due to the inhomogeneous station distribution. While terrain height can reach 2000 m or more, most stations are located below 1000 m. There is also a difference in the station density between the southern and northern parts, with a higher density in the south of Norway
Generally, HCLIM38-AROME shows an underestimation of precipitation at the south-western coast and over the Lofoten islands, while precipitation in the mountains is overestimated (Fig.
However, despite the biases found in simulated precipitation, the HCLIM38-AROME data have been successfully used by
The combined dataset shows systematically higher precipitation values than seNorge for the observation-sparse mountainous regions. As the seNorge dataset is likely to underestimate precipitation in these areas
Annual mean temperature in HCLIM38-AROME is generally lower than in seNorge, but the differences are small (Fig.
Better resolving summertime precipitation is arguably the most important reason for running CPRCMs and a primary variable for which we anticipate added value compared to coarser-resolution RCM models. In this section we discuss statistics of summer precipitation in two 10-year ERA-Interim forced climate simulations for the period 2000–2009, carried out with HCLIM38-AROME at convection-permitting scales. The focus will be on the summer half-year (April–September). The first CPRCM experiment (E1) conducted by KNMI receives lateral boundary conditions from the RCM RACMO2
As in Fig.
For the evaluation of the CPRCMs to rain radar over the Netherlands, we can only use output from E1 since the E2 domain does not cover the Netherlands. The radar data are an hourly gridded product (2.4 km horizontal resolution) that has been calibrated against automatic and manual rain gauges
We expect the largest benefits of using a high-resolution model at the finest scales, which cannot be reached by the non-CPRCM. This is confirmed for the Netherlands in Fig.
For the evaluation of the CPRCMs over Germany, we compare E1 and E2 to hourly rain gauge data. As a proxy for the rain gauge location we use the nearest model grid point. In addition, we consider only stations that have an altitude lower than 500 m (see Fig. S4 for the rain gauges involved). Similarly to the evaluation done over the Netherlands, we use the FLDMAX and FLDMEAN approach in which the appropriate statistical operator is applied over the hourly data.
The differences between CPRCMs and RCMs for FLDMAX for Germany are similar to the results for the Netherlands presented above, with the CPRCM agreeing better with observations (Fig.
The Mediterranean region is characterized by a complex morphology due to the presence of many sharp orographic features: high mountain ridges surround the Mediterranean Sea on almost every side, the presence of distinct basins and gulfs, and islands and peninsulas of various sizes. These characteristics have important consequences on both sea and atmospheric circulations because they introduce large spatial variability and the presence of many subregional and mesoscale features
We compare results from two HCLIM38 simulations, one at high resolution with the AROME configuration and another at lower resolution with the ALADIN configuration, against hourly precipitation records. These are the experiments performed.
AROME: 10 years (1990–1999), 2.5 km resolution, Iberian Peninsula ALADIN: 10 years (2005–2014), 12 km resolution, Europe
Both simulations were forced directly with ERA-Interim data at the boundaries. The simulations are compared with a dense set of around 500 automatic and manual rain gauges recording hourly precipitation that are distributed over eastern Spain (Fig. S5). These observations are extracted from the National Weather Data Bank of AEMET
The selected period for observations is 2008–2018 because hourly data from automatic stations started to be available in 2008. This period has 7 years of overlap with the HCLIM38-ALADIN simulation, but it does not overlap the HCLIM38-AROME simulation. We assume that the precipitation characteristics have not substantially changed in the last 30 years and that in two nearby 10-year periods the mean statistics calculated from hourly precipitation are comparable. The assumption that rainfall characteristics are not dependent on the period analysed, when periods are not far from each other, will be verified below and has been shown in other studies
Two aspects of summer (JJA) precipitation are studied: the timing of the maximum hourly precipitation within a day and the hourly intensity. At hourly scales we expect the convection-permitting nonhydrostatic physics (AROME) to better reproduce the precipitation characteristics compared to the hydrostatic physics (ALADIN). For the timing and intensity we calculate for each rain gauge the 10-year mean of the hour of maximum precipitation in a day and the corresponding hourly intensity, respectively. The same is calculated for the two models after interpolating the modelled precipitation to the nearest-neighbour observation. The 10-year observation mean is subtracted from the 10-year model means to get the difference in every point. These differences for every location are pooled together, and PDFs of the timing differences and hourly intensity differences are obtained for AROME and ALADIN. Gaussian distributions are fitted to the PDFs with the same mean and variance for a clearer comparison.
Several hourly thresholds have been tested (0, 5, 10, 15 and 20 mm h
The histograms of the differences of the hour of maximum precipitation and intensity between models and observations are shown in Fig.
Histograms of differences between models (HCLIM-AROME 2.5 km and HCLIM-ALADIN 12 km) and observations of the 10-year mean of
Even though HCLIM38-ALADIN has 7 years of overlap with observations and HCLIM38-AROME has none, HCLIM38-AROME is closer to the observations. Therefore, it seems that the decadal variability in the distribution is sufficiently small to allow for a comparison between observations and simulations of different (but close) periods in this case.
In conclusion, the convection-permitting model HCLIM38-AROME shows a clear improvement in the representation of key characteristics of precipitation as exemplified by timing and hourly intensity compared to the hydrostatic model HCLIM38-ALADIN. While HCLIM38-ALADIN underestimates the mean values of both variables, HCLIM38-AROME shows very small biases for the threshold used (
The Arctic region is an excellent test bed for climate models. Interactions between the atmosphere, ocean and cryosphere are central in shaping the regional climate, and biases in the simulated climate will be greatly affected by the model's ability to capture the correct build-up and melt of snow and ice. Previous studies have indicated the challenges in simulating the Arctic climate and highlighted the fact that more detailed surface schemes and very high resolution may be needed to improve model performance in the Arctic and on Greenland
HCLIM38 can run in a polar stereographic projection, which is ideal for the Arctic region. Here, we present the results from three HCLIM38 simulations, all forced by ERA-Interim during the summer of 2014 over a domain covering the Arctic region and reaching 60 ALARO24: HCLIM38-ALARO, 24 km resolution ALADIN24: HCLIM38-ALADIN, 24 km resolution ALADIN12: HCLIM38-ALADIN, 12 km resolution
This ensemble allows for the assessment of (1) the performance of HCLIM38 compared to observed conditions, (2) the differences between identical simulations with ALARO and ALADIN physics, and (3) the impact of increased resolution (in ALADIN only).
The model performance is assessed over Greenland using in situ observations at 16 locations from the Programme for Monitoring of the Greenland Ice Sheet (PROMICE;
RCM biases (with respect to CRU) in mean July–September (2000–2009) temperature and precipitation over West Africa (7.5–15
Compared to the driving ERA-Interim reanalysis data, all the HCLIM38 runs provide more detailed spatial patterns over complex terrain, such as in mountainous regions and on the slopes of the ice sheet. The comparison to the PROMICE observations reveals that all three configurations of HCLIM38 are generally colder and have lower average bias compared to the coarser ERA-Interim (approximately 80 km resolution). The PROMICE stations are all located on the slopes of the ice sheet, which causes large variability between neighbouring grid cells, making the evaluation of model runs on scales like these difficult. Even with the lapse rate correction applied, some neighbouring upper and lower stations show very different biases, even of opposite sign. As is evident from Fig.
Similarly, the melting season in all three models exhibits a general pattern of positive biases, with too many days of melting in June and negative biases with too few days in August, i.e. a shift towards the earlier part of the summer. The northernmost sites (KPC and THU), in particular, appear to have a start of the melting season that is too early in the models.
The two 24 km experiments have a very similar spatial pattern of biases across the stations, the main difference being that ALARO24 is overall warmer than ALADIN24, resulting in increased warm biases and decreased cold biases. The warmer conditions in ALARO24 are also evident in north and north-eastern Greenland on the central more elevated parts of the ice sheet. Remarkably, the mean temperature in July even exceeds the melting point in a widespread area (not shown, but the pattern is reflected in the JJA mean temperature in Fig.
The increased resolution in ALADIN12 does not result in substantial changes overall compared to ALADIN24, despite the fact that the doubled resolution is able to resolve the steep slopes better. In terms of melt days, the average total number of melt days over the summer is closer to observations in ALADIN12.
Time (local mean time) of maximum precipitation intensity during the diurnal cycle around Lake Victoria (2006) for
Previous work by
Together, these results indicate a reasonable performance of HCLIM38 in the Arctic melt season. While all three model setups tend to have the peak melting season occurring too early, the overall length of the melt season corresponds to the observed. As in previous studies, these results highlight the importance of very high resolutions for capturing the conditions on the coastal slopes of Greenland. The model–data comparison for the summer 2014 suggests a general cold bias over Greenland in HCLIM38, with ALARO physics resulting in slightly warmer conditions compared to ALADIN. While the warmer conditions in ALARO24 improve the average agreement with PROMICE observations in the ablation zone of the ice sheet, the additional warming on the north-northeastern part of the central ice sheet appears excessive compared to observations.
In the Future Resilience for African CiTies And Lands project, FRACTAL (
Since HCLIM38 has not previously been applied over the African continent, the ALADIN pan-African simulation at 50 km was compared to the CORDEX-Africa (
In order to define an optimal configuration for convection-permitting HCLIM38-AROME simulations in Africa, a number of sensitivity experiments were performed for 2005–2006 over a wider Lake Victoria region within the ELVIC CORDEX FPS. The experiments include the downscaling of ERA-Interim by HCLIM38-ALADIN at 25 km (pan-Africa and eastern Africa) and 12 km (eastern Africa) resolution. In a subsequent step HCLIM38-AROME downscaled all three HCLIM38-ALADIN simulations over the Lake Victoria Basin at 2.5 km. In the HCLIM38-ALADIN simulations precipitation over land is triggered early during the diurnal cycle compared to the Tropical Rainfall Measuring Mission (TRMM-3B42, v. 7;
HCLIM is a regional climate modelling system developed from NWP operational models. It is based on the collaboration between several European national meteorological institutes, with the main focus on the development of a CPRCM that can be used in the emerging field of convection-permitting regional climate simulations. It is a relatively new modelling system for climate applications, with the currently recommended version HCLIM38 being used in an increasing number of national and international projects. The system is developed through a series of steps, starting from the ECMWF IFS, through the ALADIN–HIRLAM operational NWP model configurations, and finally to the specific climate modelling system. As such it contains a number of possible model configurations whose terminology needs proper explanation. Hence, this paper is both timely and needed for clarity and a proper understanding of the details of the system.
HCLIM38 consists of the three main model configurations: ALADIN, ALARO and HARMONIE–AROME, originally intended to be used at different resolutions even though there is some overlap in the scales they can be applied for. They are all coupled to the same surface model, SURFEX, and use the same options for the surface. The current standard choice in HCLIM38 is ALADIN for grid spacing close to or larger than 10 km and HARMONIE–AROME for grid spacing less than 4 km. HCLIM38 has so far been used in a wide range of different climates, from equatorial to polar regions, and the initial analysis presented here indicates that it performs satisfactorily in those conditions. We also see that there are considerable benefits of using a CPRCM, particularly for sub-daily intense precipitation. HCLIM38-AROME is able to realistically simulate both the diurnal cycle and maximum intensity of sub-daily precipitation, which coarser RCMs or GCMs generally cannot accomplish because of the limitations of convection parameterizations
One of the goals of HCLIM development is to interact closely with and benefit from NWP activities in model development, particularly related to HARMONIE–AROME. There are two main causes that currently hinder direct collaboration: HCLIM lags in cycles behind NWP, and HCLIM uses more sophisticated surface and soil model parameterizations. However, in the next operational cycle, cycle 43, both of these hurdles will be greatly diminished because the NWP surface setup is going to be the same as for climate applications, and both NWP and climate researchers are developing the same model cycle for the next operational version. This benefits both groups in a number of ways. The usually smaller climate groups can focus on climate-specific questions, while the general development is done in a wider context of NWP collaboration. At the same time, climate research provides insight into long-term model biases and feedbacks usually related to physical parameterizations, which can sometimes be masked in data-assimilation-driven NWP short-range forecasts.
One common topic for both NWP and climate model developments is the aerosol treatment. In a recent work by
Other ongoing and planned HARMONIE and HCLIM developments include coupling to a wave model, an ocean model and a river-routing model using the OASIS coupler available in SURFEX
The ALADIN and HIRLAM consortia cooperate on the development of a shared system of model codes. The HCLIM model configuration forms part of this shared ALADIN–HIRLAM system. According to the ALADIN–HIRLAM collaboration agreement, all members of the ALADIN and HIRLAM consortia are allowed to license the shared ALADIN–HIRLAM codes within their home country for non-commercial research. Access to the HCLIM codes can be obtained by contacting one of the member institutes of the HIRLAM consortium (see links at:
The supplement related to this article is available online at:
DB leads the HCLIM consortium and has initiated and collated the paper. Authors HdV, AD, OL, PL, DL, RAP, JCSP, ET, BvU and FW contributed to developing or running and evaluating the model, as well as writing the paper. Authors UA, YB, EK, GL, GN, JPP, ERC, PS, EvM and MW contributed either to the model development or to model evaluation and analysis of the results. All authors commented on the paper.
The authors declare that they have no conflict of interest.
We thank Jeanette Onvlee-Hooimeijer and the anonymous reviewers for helpful suggestions about the paper. The ALADIN–HIRLAM system is developed and maintained by a large number of contributors at different institutions. We are grateful to all of them, and it is only through their continuous effort that the HCLIM development could have started. Computational and storage resources for the simulations have been provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) and the computing resource Bi provided by the Swedish National Infrastructure for Computing (SNIC) at the Swedish National Supercomputing Centre (NSC) at Linköping University. Data from the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) and the Greenland Analogue Project (GAP) were provided by the Geological Survey of Denmark and Greenland (GEUS) at
This research has been supported by Horizon 2020 (EUCP (grant no. 776613)) and the Maj and Tor Nessling foundation.
This paper was edited by Julia Hargreaves and reviewed by two anonymous referees.