# The very-high resolution configuration of the EC-Earth global model for HighResMIP

Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, et al.

► To cite this version:

Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, et al. The very-high resolution configuration of the EC-Earth global model for HighResMIP. Geoscientific Model Development, 2025, 18 (2), pp.461-482. 10.5194/gmd-2024-119. hal-04701037

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# The very-high resolution configuration of the EC-Earth global model for HighResMIP

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5 Eduardo Moreno-Chamarro¹,²*, Thomas Arsouze¹,³, Mario Acosta¹, Pierre-Antoine Bretonnière¹,

6 Miguel Castrillo¹, Eric Ferrer¹, Amanda Frigola¹, Daria Kuznetsova¹, Eneko Martin-Martinez¹,

7 Pablo Ortega¹, Sergi Palomas¹

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10 1. Barcelona Supercomputing Center (BSC), Barcelona, Spain

11 2. Now at: Max Planck Institute for Meteorology, Hamburg, Germany

12 3. Now at: CIRAD, UMR AMAP, F-34398 Montpellier, France

13 * Corresponding author: eduardo.chamarro@mpimet.mpg.de

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## 31 Abstract

We here present the very-high resolution version of the EC-Earth global climate model, EC-Earth3P-VHR, developed for HighResMIP. The model features an atmospheric resolution of ~16 km and an oceanic resolution of 1/12° (~8 km), which makes it one of the finest combined resolutions ever used to complete historical and scenario-like CMIP6 simulations. To evaluate the influence of numerical resolution on the simulated climate, EC-Earth3P-VHR is compared with two configurations of the same model at lower resolution: the ~100-km-grid EC-Earth3P-LR, and the ~25-km-grid EC-Earth3P-HR. The models' biases are evaluated against observations over the period 1980–2014. Compared to LR and HR, VHR shows a reduced equatorial Pacific cold tongue bias, an improved Gulf Stream representation with a reduced coastal warm bias and a reduced subpolar North Atlantic cold bias, and more realistic orographic precipitation over mountain ranges. By contrast, VHR shows a larger warm bias and overly low sea ice extent over the Southern Ocean. Such biases in surface temperature have an impact on the atmospheric circulation aloft, with improved stormtrack over the North Atlantic, yet worsened stormtrack over the Southern Ocean compared to the lower resolution model versions. Other biases persist with increased resolution from LR to VHR, such as the warm bias over the tropical upwelling region and the associated cloud cover underestimation, and the precipitation excess over the tropical South Atlantic and North Pacific. VHR shows improved air–sea coupling over the tropical region, although it tends to overestimate the oceanic influence on the atmospheric variability at mid-latitudes compared to observations and LR and HR. Together, these results highlight the potential for improved simulated climate in key regions, such as the Gulf Stream and the Equator, when the atmospheric and oceanic resolutions are finer than 25 km in both the ocean and atmosphere. Thanks to its unprecedented resolution, EC-Earth3P-VHR offers a new opportunity to study climate variability and change of such areas on regional/local spatial scales, in line with regional climate models.

## 57 1. Introduction

Interest in high-resolution modeling has soared in the past years, specially thanks to large European research projects and initiatives such as PRIMAVERA, nextGEMS, EERIE, and Destination Earth (last access: 20 June 2024). Broadly, these projects seek to build the next generation of high-resolution global climate (or Earth system) models capable of representing
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62 climate phenomena with unprecedented accuracy, to simulate and predict regional climate, guide
63 policymaking, and provide relevant climate information to end users. Thanks to these efforts,
64 high-resolution models at resolutions of 25–50 km or even finer have been proved to lead to
65 reduced biases in the simulated climate (see Introduction in Moreno-Chamarro et al., 2022 for a
66 review), and to a better representation of, for example, tropical cyclones (Roberts et al., 2020a;
67 Vidale et al., 2021; Zhang et al., 2021), storm-tracks (e.g., Hodges et al., 2011), the intertropical
68 convergence zone (ITCZ; e.g., Doi et al., 2012; Tian et al., 2020), or the Gulf Stream and
69 associated air–sea interactions (e.g., Kirtman et al., 2012; Bellucci et al., 2021) compared to
70 standard resolution models (hereafter, ~100-km grid). An extensive review of the benefit of
71 high-resolution modeling can be found in Haarsma et al. (2016), Hewitt et al. (2017), Roberts
72 M.J. et al. (2018), and Czaja et al. (2019). However, increased model resolution alone is not
73 always the answer: for example, persistent, well-known biases in clouds and radiation can be
74 insensitive to an increase in atmospheric resolution from a ~100-km grid to a 25–50-km grid
75 (Moreno-Chamarro et al., 2022). Inadequate model physics or insufficient tuning can thus mask
76 or negate the benefits of increased resolution.

77 High-resolution modeling faces additional challenges. One is the large computational cost
78 needed to complete the simulations, which also limits the model throughput. Both issues have
79 gradually improved thanks to steady increases in supercomputing power and parallel
80 enhancements in model efficiency to leverage that power. The community trusts in High
81 Performance Computing (HPC) to increase the performance of climate models, developing
82 different approaches to speed models up. These approaches can go from improving the
83 traditional parallelization algorithms (Tintó Prims et al., 2019a) or reducing the accuracy of the
84 variables from double to single precision (Tintó Prims et al., 2019b) to increasing the
85 Input/Output throughput of complex model configurations (Xepes-Arbós et al., 2022). Faster
86 models are also needed to complete, in a reasonable time, the tuning and the spin-up phases,
87 which for a high-resolution model, can be extremely costly. The demand for high efficiency in
88 high-resolution modeling has therefore accelerated the development and implementation of new
89 modeling strategies to ensure an optimal use of the computing resources.

90 High-resolution models also need to find a fair compromise between the resolutions of the
91 different climate components, which, sometimes, can be very disparate—for example, an
92 eddy-rich ocean model (~10 km grid) coupled to a 25 km, 50 km, or even coarser-grid
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93 atmosphere model (e.g., Gutjahr et al., 2019). Tsartsali et al. (2022), for example, reported 94 increased ocean–atmosphere coupling strength and better agreement with reanalysis and 95 observations over the Gulf Stream, when both the ocean and atmosphere resolutions are 96 increased to comparable ~25-km grid at least. Moreton et al. (2021) showed a degraded 97 representation of the air–sea interaction at increased oceanic resolution but a constant 98 atmospheric resolution. Similarly, Ma et al. (2016) found that the mesoscale ocean temperature 99 affects the storm track over the Pacific only when the atmospheric model resolution is enough to 100 resolve the small-scale diabatic heating. Finally, Rai et al. (2023) described a disproportionate 101 eddy killing when a coarse 200-km wind forcing is used to force a finer (~10–25-km) ocean, 102 compared to the case with similar grid sizes. These results of these studies thus advocate for a 103 similar resolution in both the atmosphere and ocean.

104 High-resolution modeling usually relies on single-model component, either atmospheric-only 105 (Baker et al., 2019) or ocean-only configurations (e.g., Biastoch et al., 2021), or on regional 106 models (e.g., Woollings et al., 2010; Ma et al., 2017) as in CORDEX (Jacob et al., 2014) for 107 hypothesis testing and downscaling climate projections. Such configurations, however, lack 108 global energy constraints, remote influences, and, potentially, key feedbacks rectifying the mean 109 state. These models are also limited by the boundary conditions, which often are derived from 110 coarser (~100 km) global models and can present biases in their mean climate that might be 111 absent or much reduced at a higher resolution; these biases might then be passed onto the single 112 model configurations. For example, an overly smooth Gulf Stream temperature gradient, an 113 incorrect separation, or the lack of mesoscale in ocean temperatures can impact the response of 114 the atmospheric circulation aloft (e.g., Ma et al., 2017; Lee et al., 2018). Low-resolution and 115 high-resolution global models can also respond differently to climate change: for example, the 116 northward shift and strong surface warming of the Gulf Stream projected by the eddy-rich 117 configuration of the HadGEM3-GC3.1 model for the 21st century is absent at the 118 lower-resolution model versions (Moreno-Chamarro et al., 2021). Associated with this, the 119 increase in winter precipitation is similarly much larger over Europe at the highest resolution 120 than at any lower one, which reinforces the idea that the response of the atmosphere is strongly 121 sensitive to the boundary conditions. These findings put a limit to our confidence in single-model 122 configurations and regional models, since they lack a global dynamical response.
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As a response to the listed challenges, we here present the eddy-rich version of the EC-Earth
climate model for PRIMAVERA/HighResMIP. This is likely one of the finest combined
horizontal resolution global models ever used to complete CMIP-like simulations, with a
nominal resolution of about 10–15 km; it also has the additional advantage that the resolution is
comparable in both the atmosphere and ocean/sea-ice, which allows the atmosphere to “see” the
fine-scale forcing from the ocean with minimal information lost from interpolation. In this paper,
we describe the model configuration and the developments in model efficiency (Section 2), as
well as the main characteristics of its climate for the period 1980–2014 compared to observations
(Section 3).

132

133 **2. Model Description and Experimental Setup**

134 **2.1 Model description**

All HighResMIP contributions with the EC-Earth global coupled climate model have been
performed with its version 3.2.2, developed within the PRIMAVERA project (EC-Earth3P). The
model consists of the atmosphere, ocean, and sea ice components. The atmosphere model is
based on the ECMWF Integrated Forecasting System (IFS), in the 36r4 cycle. A detailed account
of the changes introduced in this cycle can be found on the ECMWF website
(https://confluence.ecmwf.int/display/FCST/Implementation+of+IFS+Cycle+36r4), last access:
20 June 2024). The very-high resolution version of the model, EC-Earth3P-VHR, features a
triangular truncation at wave number 1279 (hence known as T1279) in spectral space, with a
linear N640 reduced Gaussian grid. This corresponds to a spacing of ~16 km. However, because
of the complexity of numerical solutions and parametrizations, the effective resolution (this is the
smallest scale IFS T1279 can fully resolve) is of ~120 km (Abdalla et al., 2013). Vertically, the
model features 91 levels, resolving the middle atmosphere up to 0.01 hPa. The model time step
during the simulation was 360 s. IFS integrates the revised land surface hydrology Tiled
ECMWF Scheme for Surface Exchanges over Land (H-Tessel) model (Balsamo et al., 2009;
Hazeleger et al., 2012).

The ocean model is the Nucleus for European Modelling of the Ocean in its version 3.6
(NEMO3.6; Madec, 2008, Madec and the NEMO team, 2016). This is a hydrostatic,
finite-difference, free-surface, primitive equation general circulation model. EC-Earth3P-VHR
uses the ORCA12 tripolar grid, with the horizontal resolution increasing from the Equator to the
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154 poles: ~9 km at the Equator, ~7 km at mid-latitudes, and ~2 km near the poles. This corresponds 155 to an effective resolution of ~45 km (roughly five times the ORCA grid spacing). The model 156 uses a z* coordinate system for the vertical grid and has 75 vertical levels, with the resolution 157 decreasing from 1 m at the surface to 200 m in the deep ocean. The bottom topography is derived 158 from the combination of ETOPO1 (Amante and Eakins, 2009) and GEBCO_08 (Becker et al., 159 2009). The sea ice model is the Louvain-la-Neuve sea Ice Model in its version 3 (LIM3) 160 (Vancoppenolle et al., 2012). This is a dynamic-thermodynamic sea ice model, with five ice 161 thickness categories. The time steps are 240 s for NEMO3.6, and 720 s for LIM3 in the 162 EC-Earth3P-VHR.

163 The atmosphere-land and ocean-sea-ice components are coupled through the OASIS 164 (Ocean, Atmosphere, Sea Ice, Soil) coupler, version 3 (Valcke and Morel, 2006; Craig et al., 165 2017). The remapping of runoff from the atmospheric grid points to runoff areas on the ocean 166 grid was re-implemented to be independent of the grid resolution. This was done by introducing 167 an auxiliary model component and relying on the interpolation routines provided by the OASIS 168 coupler.

169 EC-Earth3P-VHR (hereafter, VHR) is compared with two lower-resolution global model 170 versions, also run within the PRIMAVERA/HighResMIP project: EC-Earth3P (hereafter, LR; 171 EC-Earth Consortium, 2019), and EC-Earth3P-HR (hereafter, HR; EC-Earth Consortium, 2018). 172 In the atmosphere, they use the T255 (~107 km) and T511 (~54.2 km) spectral resolution of the 173 IFS model respectively (equivalent to an effective resolution of ~600 km and ~280 km 174 respectively; Abdalla et al., 2013), both with 91 vertical levels. In the ocean, LR and HR use the 175 ORCA1 (~100 km) and ORCA025 (~25 km) tripolar grid respectively (equivalent to an effective 176 resolution of ~500 km and ~125 km respectively), both with 75 vertical levels. They both use the 177 LIM3 sea ice model and the OASIS coupler as well. LR and HR's time steps are respectively 178 2700 s and 900 s in all the atmosphere, ocean, and sea ice. More details of these two other model 179 versions can be found in Haarsma et al. (2020).

180 Following the CMIP6 HighResMIP protocol, no additional tuning is applied across 181 resolutions but for a short list of parameters that explicitly change with resolution, particularly 182 for oceanic diffusion and viscosity. The higher resolution in the atmosphere results in a better 183 representation of features such as tropical storms, land/sea transitions, heavy rainfall, and fronts 184 (see Fig. 1 as an example), while in the ocean the increase in resolution allows mesoscale
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185 processes to be resolved at a much larger range of latitudes and the representation of finer
186 resolution bathymetric features and coastlines.

187 **2.2 Configuration and workflow setup and performance optimization**

188 The development and maintenance of the EC-Earth model is supported by the EC-Earth
189 Consortium, which shares model code, configurations, and minimal software infrastructure to
190 operate it. While the LR and HR configurations of EC-Earth-3P were developed in a broad
191 collaboration of all the consortium members participating in PRIMAVERA, VHR's development
192 was primarily completed at the Barcelona Supercomputing Center, in collaboration with the
193 Swedish Meteorological and Hydrological Institute (SMHI) within the ESiWACE2 H2020
194 project (last access: 20 June 2024). The development was conducted on two different
195 supercomputing machines: MareNostrum3, and MareNostrum4 (last access: 20 June 2024).
196 VHR's configuration, at the time of the project, represented one of the most cutting-edge
197 versions of a climate model to run over long time scales. Obtaining a production version of the
198 model, however, entailed the development of novel source code and execution scripts, the
199 generation of all requisite files for initializing the simulations, and the adaptation of the model
200 workflow software. This presented a significant challenge for both the operations department and
201 the workflow developers, which were required to fine-tune the system to achieve stable runs and
202 minimize the loss of computing hours. For example, generating the interpolation weight files to
203 couple the new model grids for the OASIS coupler was particularly challenging. This process
204 could not readily be parallelized at that time and required collaborating with the OASIS
205 development group. For the workflow, a significant proportion of the effort was devoted to
206 integrating the dedicated data transfer nodes available in the MareNostrum4 cluster into the
207 workflow. Additionally, the automatic algorithm that enables the suppression of land grid
208 subdomains in the NEMO ocean model was incorporated, resulting in a reduction of about 12%
209 in the required HPC resources. Finally, the MareNostrum4 new network, despite its fast and
210 responsive nature, proved to be quite unstable when subjected to high workloads involving
211 multiple concurrent communications, as was the case of the VHR configuration. At the end of
212 the ESiWACE2 project (December 2022), all the code was versioned and shared with the other
213 partners within the EC-Earth Consortium.
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a. ERA5

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b. VHR

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c. HR

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d. LR

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214

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216 **Figure 1.** Snapshot of an extratropical storm over the North Atlantic in the winter 1999–2000 in 217 a) ERA5, and in the b) VHR, c) HR, and d) LR models on their original grids. Shown are daily 218 precipitation rate (mmd⁻¹; blue shading), cloud cover (% of area; gray shading), and sea-level 219 pressure (hPa; contours).
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220 Once deployed, the workflow needed to be made more efficient to be put into operation.
221 Emerging advancements in global climate modeling demand heightened focus on HPC,
222 particularly to accommodate the increasing need for enhanced model resolution (Acosta et al.,
223 2024). An example of such demanding requirements is the VHR configuration, underscoring the
224 need for efficient resource use. In order to address this issue, we conducted a two-fold HPC
225 performance exercise, which involved both a pure computational performance analysis and a
226 scalability study for each model component (IFS and NEMO), complemented with a load
227 balance optimization for the coupling. This analysis concluded that the coupling and output
228 process could be a bottleneck. An optimization was included to package different coupling fields
229 to be sent in the same MPI (Message Passing Interface) communications, reducing the latency
230 and taking advantage of the bandwidth. Additionally, the I/O (Input/Output) setup was optimized
231 to ensure minimal time was needed to produce the outputs. While the primary objective of the
232 scalability and load-balance study was to assess the model's efficiency and determine an optimal
233 resource utilization, findings by Acosta et al. (2023) also indicate that enhancing the
234 performance of one component, such as reducing the execution time of IFS, may not necessarily
235 decrease the overall execution time of the coupled model. This discrepancy could stem from a
236 synchronization point at the end of each coupled time step, where both components exchange
237 fields. In cases where other non-optimized components lag behind, a load rebalance becomes
238 necessary.

239 We ran a series of scalability tests to balance the resources (computing cores) of the VHR's
240 IFS and NEMO models (Fig. 2). To find the most balanced configuration for a given amount of
241 resources, we followed two different but complementary approaches. The first and most costly
242 one tried to find the optimal distribution by assigning the same number of processors to IFS and
243 NEMO first, and moving resources between them alternately; this allowed identifying the
244 intervals for which the model performance increases by using variations of half-interval search
245 algorithm. The second approach to balance the configuration started from one separate
246 scalability test for each model component that was later used to determine the optimal
247 configuration.

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250
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251

252 **Figure 2.** Results of the scalability test of the VHR configuration (T1279 IFS and ORCA12 NEMO) at MareNostrum4 (blue line) in simulated years per day (SYPD) for a given amount of processors. The orange line shows the ideal case with no loss in computing performance.

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256 The simulations described here were configured and run with the workflow manager 257 Autosubmit (Manubens-Gil et al., 2016). This Python toolbox facilitates the production of 258 numerical experiments, like the EC-Earth ones. It creates an oriented graph, taking into account 259 every step of the workflow, including data pre- and post-processing, the transfer to storage 260 spaces, or the conversion of the output data to CMOR standard, with details on computing 261 resources needed for each step. Autosubmit also allows easily handling experiments with 262 different members, start dates, and initial conditions.

263

## 2.3 Simulations

265 The VHR simulations follow the HighResMIP experimental protocol (Haarsma et al., 2016) and 266 consist of: i) a 50-year spin-up run (spin-up-1950), with initial conditions of temperature and 267 salinity from an ocean state representative of the 1950s (Good et al., 2013, EN4 data set) and 268 forcing consisting of well-mixed greenhouse gases, including O$_3$ and aerosol loading for a 1950s 269 (~10-year mean) climatology; ii) a 105-year control run (control-1950), starting from the end of
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270 spin-up-1950 and keeping the same fixed forcing; iii) the historical run (hist-1950), starting from
271 the same initial state as the control, but with time-varying external forcing for the period
272 1950–2014; iv) and the future scenario run (highres-future), as a continuation of the historical
273 simulation under the CMIP6 SSP5-8.5 scenario (Kriegler et al., 2017) for the period 2015–2050.
274 In this work, VHR's hist-1950 simulation is compared with corresponding hist-1950 runs from
275 LR and HR (Haarsma et al., 2020).

276 During the model setup, we erroneously applied the EN4 initial conditions at the beginning
277 of all the spin-up runs. While EN4 uses practical salinity and potential temperature, the NEMO
278 model, which uses the TEOS-10 equation of state, requires absolute salinity and conservative
279 temperature. Nonetheless, the differences between the two temperature and salinity types is
280 indeed small (Pawlowicz, 2013; McDougall et al., 2021), and we expect the error to minimize
281 throughout the spin-up.

282

283 **2.4 Observations and reanalysis**

284 As we mainly aim to evaluate the performance of EC-Earth3P-VHR configuration and describe
285 the main model biases and characteristics, we focus on the best-observed part of the historical
286 period of the historical simulations, between 1980 and 2014. The three model configurations are
287 compared with the following observational and reanalysis data: near-surface (2 m) air
288 temperature (SAT), zonal winds, sea-level pressure, and turbulent fluxes from the ERA5
289 reanalysis (Hersbach et al., 2020); precipitation rate from the version-2 GPCP dataset (Adler et
290 al., 2003); cloud cover from the version-3 ESA Cloud_cci dataset (ESA CCI-CLOUD; Stengel et
291 al., 2020); potential temperature and salinity of the ocean from the Hadley Center EN4 (version
292 4.2.2; Good et al., 2013); sea ice concentration from OSI SAF (OSI-409/OSI-409-a;
293 EUMETSAT Ocean and Sea Ice Satellite Application Facility, 2015); and sea ice volume from
294 GIOMAS (Global Ice-Ocean Modeling and Assimilation System; Zhang and Rothrock, 2003).
295 The period of comparison maximizes data availability and is therefore 1980–2014 for all the
296 cases but for the GPCP dataset (1983–2014) and the ESA CCI-CLOUD dataset (1982–2014).
297 Biases in sea-surface temperature (SST) are very similar to those in SAT and are therefore not
298 shown.

299

300
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301 **3. Results**

302 **3.1 Spin-up phase**

303 Across all three model resolutions, the length of the spin-up (50 years) appears to be insufficient
304 to equilibrate the full ocean (Fig. 3b); in fact; the ocean temperature is still drifting about
305 0.001–0.002 °C/yr (computed over the last 50 years) towards warmer conditions at the end of the
306 control simulation in the three configurations. In the upper ocean, however, VHR shows the
307 smallest warming drift of the three configurations: about 0.00005 °C/yr compared to 0.0025
308 °C/yr and 0.0062 °C/yr in HR and LR, respectively (computed over the last 50 years; Fig. 3a). It
309 is therefore safe to say that an analysis focused on the upper ocean and on the air–sea interface
310 will enjoy a relatively stable climate in the control simulations. In the historical simulations, the
311 warming of the ocean accelerates due to the CO₂ forcing; after 64 years (year 114 in Fig. 3), the
312 whole ocean warming reaches similar values to those at the end of the control simulations after
313 100 years in the three model resolutions. Near the surface, the warming trend is much larger. Of
314 the three configurations, VHR is the one with the smallest drift in the control run and the
315 smallest ocean warming in the historical period. Although the three runs start from similar initial
316 conditions derived from an EN4 climatology (Section 2.3), VHR is ~0.4 °C warmer near the
317 surface than LR and HR, especially over the spin-up period. This is likely related to the
318 development of a widespread warm bias over the Southern Ocean (Fig. 4), which we discuss in
319 detail in Section 3.6.

320 In the following Sections, we describe the main characteristics of the VHR compared to LR
321 and HR by focusing on particular regions and biases. This approach should help us highlight the
322 benefits, or lack thereof, due to increased resolution. The main biases in the three model
323 configurations are compared with the observational data set listed in Section 2.4.

324
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325

326 **Figure 3.** Mean oceanic temperature in the LR (yellow), HR (red), and VHR (blue) models in the spin-up runs (0–50-year period), control runs (50–150-year period; solid lines), and historical runs (50–114-year period; dashed lines) in a) the upper 100 m, and b) the whole ocean. The vertical dashed line marks the end of the spin-up period

330

331 **3.2 Tropics**

332 A warm bias of 1–2 K is present over the subtropical upwelling regions along the South American and African coasts in the three configurations and shows small variations across them (Fig. 4). The increase in resolution in VHR has thus no clear benefit to reduce it. Past studies
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335 have related this bias to an underestimation of the stratocumulus cloud deck (Richter, 2015). This 336 also seems to be the case in the three models, which all show negative cloud biases by about 20 337 % over all the subtropical upwelling areas, specially along the subtropical Pacific and Atlantic 338 western coasts (Fig. 5). A better resolved orography near the region does not contribute to 339 reducing the bias either, as suggested in previous studies (Milinski et al., 2016): for example, 340 although VHR shows reduced temperature biases along the Andes compared to HR and LR, it 341 has no effect on the biases over the eastern subtropical Pacific upwelling.

342 Overall, VHR shows reduced tropical precipitation biases compared to HR and LR (Fig. 6).
343 This is the case, for example, for the double ITCZ bias: this bias is usually characterized by a 344 precipitation excess over the central tropical North Pacific and the western tropical South Pacific 345 and a precipitation deficit over the equatorial Pacific, as LR clearly shows. The dry area over the 346 Equator is reduced with resolution, and the anomaly is even non-significant in VHR. This is a 347 clear improvement from increased resolution, and it can be related to a reduced cold bias over 348 the Equator (Fig. 4). In contrast, the precipitation excess over the tropical North Pacific and the 349 Maritime Continent persists into VHR, with only minor reductions of 1–2 mmd⁻¹ compared to 350 HR and LR (Fig. 6). The precipitation excess over the tropical North Pacific suggests a seasonal 351 cycle reaching too far north, while the excess over the Maritime Continent, together with that 352 over the western tropical Atlantic and Indian oceans, suggests an excess in convective 353 precipitation over very warm waters.

354 Over the tropical Atlantic, the precipitation bias pattern points to an ITCZ anchored to the 355 south-western part and not reaching the Sahel area. This bias is somewhat reduced in VHR 356 compared to HR and LR, although not entirely removed. Over land, the dry bias over North 357 Brazil and the wet bias along the Andes are not reduced with resolution, either. These positive 358 and negative precipitation biases appear together with positive and negative biases in cloud 359 cover, respectively, related to an overestimation or underestimation in convective clouds (Fig. 5).

360

361

362

363

364

365
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a. VHR

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b. HR

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c. LR

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366

367 **Figure 4.** Bias in SAT (in K) with respect to ERA5in the a) VHR, b) HR, and c) LR models for the period 1980–2014. Stippling masks anomalies that are not significant at the 5 % level.

369
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a. VHR

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b. HR

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c. LR

<figure><img src="image_38.png" /><figcaption>370 **Figure 5.** Bias in cloud cover (in %) with respect to ESA CCI-CLOUD (contours in all the 371 panels; in %) in the a) VHR, b) HR, and c) LR models for the period 1982–2014. Stippling 372 masks anomalies that are not significant at the 5 % level.</figcaption></figure>
<figure><img src="image_39.png" /></figure>

<figure><img src="image_40.png" /></figure>

a. VHR

<figure><img src="image_41.png" /></figure>

b. HR

<figure><img src="image_42.png" /></figure>

c. LR

<figure><img src="image_43.png" /><figcaption>374 **Figure 6.** Bias in precipitation rate (in mmd⁻¹) with respect to GPCP (contours in all the panels; 375 in mmd⁻¹) in the a) VHR, b) HR, and c) LR models for the period 1983–2014. Stippling masks 376 anomalies that are not significant at the 5 % level.</figcaption></figure>
<figure><img src="image_44.png" /></figure>

<figure><img src="image_45.png" /></figure>

378 **3.3 Northern Hemisphere mid- and high-latitudes**

379 The largest improvement in the simulated climate from LR to VHR is over the North Atlantic.
380 From south to north, the Gulf Stream representation is much improved in VHR compared to HR
381 and LR, with sharper gradients in temperature and sea-surface height (not shown). The position
382 of the Gulf Stream separation is also improved, which leads to a reduction of the warm bias
383 along the US East Coast from LR to VHR (Fig. 4). A paper on a dedicated analysis of the biases
384 over the North Atlantic along the Gulf Stream is currently in preparation.

385 Farther north, the widespread cold bias up to about 6 K in LR is strongly reduced in HR, and
386 even further in VHR, which is the configuration closest to observations (Fig. 4). The cold bias in
387 LR is related to an unrealistically large sea ice extent, which covers the entire Labrador Sea and
388 the western part of the subpolar North Atlantic (Figs. 7 and 8). The reduction of the cold bias
389 between LR and VHR bias has a deep impact on the climate of the North Atlantic. In the
390 atmosphere aloft, it improves the representation of the boreal winter (DJF) stormtrack (Fig. 9)
391 and jet (Fig. 10). The boreal winter stormtrack is overestimated over the subpolar North Atlantic,
392 particularly over the eastern part, in LR, likely related to an excessively strong meridional
393 temperature gradient; by contrast, VHR stormtrack is much closer to ERA5 over the North
394 Atlantic. In the ocean, excessive sea ice leads to a negative salinity bias above 2 psu in the
395 subpolar North Atlantic in LR, which is much reduced in VHR (Fig. 11). Two mechanisms can
396 explain this fresh bias in LR: on the one hand, a reduced oceanic salinity transport from
397 subtropical latitudes by a weakened subpolar gyre (not shown); on the other, errors in the
398 seasonal cycle of the sea ice, during which ice melting would cause an anomalous freshwater
399 input in regions where it is not observed. The negative bias in surface salinity propagates into
400 deeper levels, especially between 300 m and 1000 m in the Arctic (Fig. 12). Similarly, the warm
401 subsurface bias at around 40 °N might also be related to the sea ice excess in the subpolar North
402 Atlantic in LR (Fig. 11). Expanded sea ice in LR causes weaker subpolar gyre strength and
403 associated northward heat transport (not shown), leading to heat accumulation in the intergyre
404 region. However, although this bias is reduced at higher resolutions in HR and VHR, it is still
405 present, suggesting other deficiencies in the formation of intermediate waters in the North
406 Atlantic. The overly large sea ice cover also hampers oceanic deep mixing in the Labrador Sea in
407 LR, whose main region of deep water formations are in the Nordic Seas instead (Fig. 13).
408 Oceanic deep mixing takes larger values above 1000 m in VHR and HR in the Labrador Sea. A
<figure><img src="image_46.png" /></figure>

409 detailed analysis of the characteristics and driving mechanisms of the deep water formation in
410 the Labrador Sea across the three resolutions and compared to observations is currently in
411 preparation.

412

<figure><img src="image_47.png" /></figure>

413

414 **Figure 7.** Sea ice concentration (in % of area) in the VHR model (gray shading) for the period 1980–2014. Contours are the 15-% value in the LR (orange), HR (red), and VHR (blue) models, as well as in OSI SAF (black) for the period 1980–2014. Top/bottom panels are for the Arctic/Antarctic in March (left) and September (right).

418
<figure><img src="image_48.png" /></figure>

<figure><img src="image_49.png" /><figcaption>Figure 8. Monthly climatology in the sea ice extent (in $10^6$ km$^2$; top) and volume (in $10^3$ km$^3$; bottom) in the Arctic (left) and Antarctica (right) in the LR (yellow), HR (red), and VHR (blue) models, as well as in OSI SAF, for sea ice extent, and GIOMAS, for the volume, for the period 1980–2014.</figcaption></figure>

424

425

426

427

428

429

430
<figure><img src="image_50.png" /></figure>

<figure><img src="image_51.png" /><figcaption>431 **Figure 9.** Bias in winter stormtrack, computed as the standard deviation of the 2–6 d band-pass 432 filtered daily sea-level pressure (in Pa) with respect to ERA5 (contours in all the panels; in Pa) in 433 the a) VHR, b) HR, and c) LR models for the period 1980–2014. Each panel show anomalies in 434 the boreal winter (DJF; top) and austral winter (JJA; bottom)</figcaption></figure>
<figure><img src="image_52.png" /></figure>

<figure><img src="image_53.png" /><figcaption>436 **Figure 10.** Bias in winter zonal wind at 250 hPa (in ms⁻¹) with respect to ERA5 (contours in all 437 the panels; in ms⁻¹) in the a) VHR, b) HR, and c) LR models for the period 1980–2014. Stippling 438 masks anomalies that are not significant at the 5 % level. Each panel show anomalies in the 439 boreal winter (DJF; top) and austral winter (JJA; bottom) 440</figcaption></figure>
<figure><img src="image_54.png" /></figure>

a. VHR

<figure><img src="image_55.png" /></figure>

b. HR

<figure><img src="image_56.png" /></figure>

c. LR

<figure><img src="image_57.png" /></figure>

441

442 **Figure 11.** Sea-surface salinity bias (in psu) with respect to EN4 (contours in all the panels; in

443 psu) in the a) VHR, b) HR, and c) LR models for the period 1980–2014. Stippling masks

444 anomalies that are not significant at the 5 % level.
<figure><img src="image_58.png" /></figure>

<figure><img src="image_59.png" /><figcaption>446 **Figure 12.** Bias in ocean potential temperature (in K; top) and in salinity (in psu; bottom) with 447 respect to EN4 (contours in all the panels; in K, top, and psu, bottom) in the a) VHR, b) HR, and 448 c) LR models for the period 1980–2014. Stippling masks anomalies that are not significant at the 449 5 % level. Each panel is separated into the upper and lower 500 m.</figcaption></figure>

450
<figure><img src="image_60.png" /></figure>

<figure><img src="image_61.png" /><figcaption>Figure 13. Mixed layer depth (in m) in the a) VHR, b) HR, and c) LR models for the period 1980–2014. Northern Hemisphere and Southern Hemisphere values are for March and September, respectively.</figcaption></figure>
<figure><img src="image_62.png" /></figure>

<figure><img src="image_63.png" /></figure>

Weak deep mixing results in a relatively weak Atlantic Meridional Overturning Circulation (AMOC; Fig. 14) in LR. The AMOC strength increases with resolution, related to the reduction of the cold bias and sea ice extent bias over the subpolar North Atlantic. The strength of the AMOC in VHR is thus the closest to the observed RAPID strength at 26 °N (17 ± 3 Sv; Frajka-Williams et al., 2019) among the three models: 14 ± 3 Sv in VHR, 12 ± 4 Sv in HR, and 11 ± 2 Sv in LR (computed from monthly streamfunction at 26 °N for the period 2004–2014). The structure of the AMOC cell is similar in the three model configurations, with a main positive cell in the upper 3000 m up to 60 °N and with a maximum at around 30 °N, and a negative deeper one below with a strength of 2–4 Sv.

<figure><img src="image_64.png" /><figcaption>Figure 14. Atlantic overturning streamfunction (in Sv) in the a) VHR, b) HR, and c) LR models for the period 1980–2014.</figcaption></figure>
<figure><img src="image_65.png" /></figure>

<figure><img src="image_66.png" /></figure>

In HR, and even more in VHR, the cold bias over the Labrador Sea is replaced by a warm bias (Fig. 4), up to 3–4 K in VHR. This bias also appears in other eddy-rich climate models, related to a stronger Atlantic ocean heat transport than at lower resolutions (Roberts et al., 2020b). Over the Nordic Seas, by contrast, a cold bias is present in the three models, although it is somewhat reduced at VHR by 1–2 K compared to LR and HR (Fig. 4). In the three cases, this bias is related to an excessively large sea ice cover in the region (Fig. 7). The warm bias over the Labrador Sea and cold bias over the Nordic Seas in VHR might suggest a misrepresentation of the distribution of oceanic heat transport between the two basins, favoring the westward transport over the northward across-Ridge heat transport. It might also or instead be related to a misrepresentation of the sea ice drift across the Denmark Strait (Gutjahr et al., 2022). Relatively weak transport across the Strait would lead to ice deficit in the Labrador Sea, and hence warming, and to ice accumulation in the Nordic Seas, hence cooling.

On a hemispheric scale, the three models simulate a slightly low Northern Hemisphere sea ice extent, mainly due to the underestimation of the sea ice cover in the Sea of Okhotsk, Baltic Sea, and Labrador Sea in HR and VHR (Fig. 8). By contrast, the three models show an overly large sea ice volume by about $10^4 \text{ km}^3$ compared to GIOMAS (Fig. 9), as they all simulate very thick sea ice in the central Arctic (not shown). Anomalously thick ice in the models leads to an excess of brine rejection (not shown), which can explain the positive salinity bias above 2 psu in the upper 100–200 m of the Arctic Ocean (Figs. 11 and 12). In VHR, the associated increase in upper-ocean density leads to deeper oceanic mixing than in LR or HR, with a mixed layer depth in the central Arctic that can reach up to 1000 m (Fig. 13).

Over the Pacific, biases tend to be weaker than over the Atlantic. A warm bias of about 1 K develops over the subpolar North Pacific from LR to VHR (Fig. 4), which could explain the negative bias in boreal winter (DJF) stormtrack aloft (Fig. 9) and the weaker jet stream over the central Pacific in VHR (Fig. 10).

Over land, the cold bias over the Sahara is reduced with increased resolution (Fig. 4). Similarly, the cold biases over large mountain ranges, such as the Rockies, the Andes, and the Himalaya, up to about several degrees in LR are much reduced in VHR, related to better resolved orography.

496

497
<figure><img src="image_67.png" /></figure>

<figure><img src="image_68.png" /></figure>

498 **3.4. Southern Ocean**

499 The Southern Ocean is the region where VHR performs the worst compared to HR and LR. The 500 warm bias over the Southern Ocean increases with resolution, up to 4–5 K in VHR, compared to 501 1–2 K and 2–3 K for HR and LR respectively (Fig. 4). It tends to be largest over the Atlantic and 502 Indian sectors of the Southern Ocean and close to the Antarctic coast. Although the warm bias 503 remains generally confined to the upper 100–200 m at around 60 °S, it might also be connected 504 to the warm bias at depth between 2000 m and 4000 m (Fig. 12).

505 Two main mechanisms could explain the Southern Ocean warm bias: VHR has the largest 506 cloud cover underestimation of the three models, especially over the Atlantic and Indian sectors, 507 up to 15 % in VHR compared to 5–10 % in LR and HR (Fig. 5). Previous studies have related 508 the Southern Ocean warm biases to misrepresentation and underestimation of the mixed-phase 509 clouds, which lead to an excess of shortwave radiation reaching the surface, thereby warming it 510 (e.g., Hwang, and Frierson, 2013; Hyder et al., 2018). Connected to the warm bias, VHR also 511 shows the lowest sea ice extent of the three resolutions all year round (Figs. 7 and 8). Although 512 the three models underestimate the Antarctic sea ice extent, in VHR this is nearly half as in 513 observations for the same period (OSI SAF, 1980–2014). In terms of sea ice volume (Fig. 8), 514 however, LR shows larger values by about 2·10³ km³ than GIOMAS between November and 515 April, pointing to overly thick sea ice. As for the extent, VHR also shows the lowest sea ice 516 volume, nearly half of the values in GIOMAS. The three models show the maximum volume one 517 month later than in GIOMAS, in October rather than in September. This contrasts with the 518 Arctic, where the three models capture the general shape of the seasonal cycle.

519 The surface warming over the Southern Ocean leads to a widespread underestimation of the 520 stormtracks (Fig. 9) and jet stream (Fig. 10) in the austral winter (JJA) in HR and, especially, in 521 VHR, compared to LR, which is much closer to ERA5. Although precipitation is also 522 underestimated over the Southern Ocean, specially in VHR, this is not a particularly strong bias, 523 at least compared to those over the tropical regions (Fig. 6).

524 Late austral summer (September) deep mixing tends to increase by about 200 m from LR to 525 HR and VHR, especially in the Pacific sector. These two latter resolutions show similar deep 526 mixing mean state, with variations only due to resolution and the better representation of the 527 mesoscale in VHR (Fig. 13). The underestimation of the stormtrack over the Southern Ocean 528 therefore does not seem to have an impact on the oceanic mixing below in VHR.
<figure><img src="image_69.png" /></figure>

<figure><img src="image_70.png" /></figure>

529 **3.5 Air–sea coupling**

530 We compare the change in the intensity of air–sea coupling from LR to VHR via the computation
531 of cross-correlation coefficients of the deseasonalized monthly SST and net surface energy flux
532 (Fig. 15). This analysis has extensively been used to study regions in which the ocean tends to
533 drive atmospheric variability (correlation coefficient values approaching one) or vice versa
534 (correlation coefficient values close to zero; e.g., Bishop et al., 2017; Small et al., 2019). The
535 three model configurations are compared with the ERA5 reanalysis, as done in the previous
536 Sections for the biases. To complement the analysis with a non-model based product, we also
537 include satellite observations of radiative fluxes from J-OFURO3 (Tomita et al., 2019). The two
538 products show an overall good agreement, with areas of large correlation coefficient values at the
539 Equator, along the western boundary currents, and over the Southern Ocean (Fig. 15a,b). These
540 areas, nonetheless, tend to be broader in J-OFURO3 than in ERA5.

541 Over the tropics, the three configurations tend to underestimate the coupling around the
542 Equator, although they all reproduce well the band of correlation coefficients of high values
543 along the equatorial Pacific and Atlantic. However, this band is narrower in LR and HR over the
544 subtropics than it is in ERA5 and J-OFURO3. VHR is thus the closest configuration to the two
545 reference observational products in the region. This result highlights the need for a model
546 resolution finer than 25 km in both the ocean and atmosphere to represent realistic tropical
547 climate interactions, in agreement with conclusions in Section 3.2.

548 At mid-latitudes, the coupling is greatly improved in HR and VHR compared to LR,
549 particularly over the subpolar regions compared to ERA5 and J-OFURO3. LR shows a rather
550 smooth pattern, with very low values in key regions over the Gulf Stream, Kuroshio Current, and
551 Southern Ocean, which suggests a standard 1° resolution is insufficient to represent a realistic
552 air–sea coupling. VHR and HR show, by contrast, sharper gradients in the correlation coefficient
553 values close to 1 over those regions. This result is consistent with previous studies, which also
554 found a degradation of the air–sea coupling in coarse grids, especially above 1° (e.g., Small et
555 al., 2019). However, VHR shows unrealistic broader areas of higher correlation coefficient
556 values than ERA5 and J-OFURO3 at mid-latitudes, degrading results from HR. One hypothesis
557 for this discrepancy might result from the difference of IFS grid resolution between VHR
558 (T1279) and ERA5 (T639), since the relationship between SST and turbulent fluxes shows
559 certain scale dependency (e.g., Small et al., 2019; Sun and Wu, 2022). However, results do not
<figure><img src="image_71.png" /></figure>

560 improve even when regridding VHR onto ERA5 grid before computing the correlation
561 coefficients (not shown). A second hypothesis is the lack of the ocean current feedback in VHR,
562 hence the lack of eddy-killing, which can control the simulated Gulf Stream's dynamics and
563 energy pathways (Renault et al., 2023). However, the pattern of correlation coefficient values
564 remains relatively unchanged when it is computed with a VHR configuration that includes a
565 parameterization that considers the wind adjustment to the ocean current feedback (not shown)
566 (Renault et al., 2019). The results suggest that the VHR's ocean exerts a stronger and more
567 widespread influence on the atmosphere variability than in HR and LR.

568 Further north, air-sea coupling is overestimated in all the models over the Nordic Seas, likely
569 related to the excess in sea ice in the region and its changes over the seasonal cycle. Together, the
570 results suggest that a realistic air-sea coupling requires grids finer than 1/4° at least, with
571 potential local improvements on a 1/12° grid, especially over the Tropics.

572

573 **4. Discussion and Conclusions**

574 This paper presents the eddy-rich configuration of the EC-Earth3P-VHR global model for
575 HighResMIP. We describe both the necessary technical developments to run the model
576 efficiently, and the main features of the simulated climate compared to recent observations
577 (1980–2014 period) and to two lower-resolution model configurations (the eddy-present,
578 ~25-km-grid EC-Earth3P-HR; and the non-eddy, ~100-km-grid EC-Earth3P-LR). The
579 EC-Earth3P-VHR (or VHR) uses a comparable atmospheric and oceanic resolution of 10–15 km
580 in a global fully coupled setup, which is, to our knowledge, one of the finest combined grids ever
581 used to date to perform long climate integrations for CMIP. Our focus here is on the
582 HighResMIP historical simulation (HighResMIP’s hist-1950). This run is part of a larger set of
583 runs, which includes a spin-up and control runs (HighResMIP’s control-1950), a future extension
584 under the ssp8.5 scenario (HighResMIP’s highres-future), three hosing simulations forced by
585 idealized Greenland melting, and AMIP sensitivity simulations, all performed within the
586 European PRIMAVERA project and the Spanish STREAM project. Those additional simulations
587 will be described in their corresponding publications, which are currently in preparation.
<figure><img src="image_72.png" /></figure>

<figure><img src="image_73.png" /></figure>

<figure><img src="image_74.png" /></figure>

<figure><img src="image_75.png" /></figure>

<figure><img src="image_76.png" /></figure>

<figure><img src="image_77.png" /></figure>
<figure><img src="image_78.png" /></figure>

<figure><img src="image_79.png" /></figure>

The comparison across the three resolutions (this is, VHR, HR, and LR), all with the same physics and no additional tuning, allows identifying regions where increased resolution improves the model performance with respect to observations. One of those regions is the Tropics, and specially the equatorial Pacific, where the cold tongue bias and the dry bias above are both reduced in VHR compared to HR and LR. Wengel et al. (2021) also reports a similar bias reduction in an eddy-resolving configuration of the CESM (0.25° resolution in the atmosphere, 0.1° resolution in the ocean), which they link to better represented mesoscale features, such as tropical instability waves. Similarly, the HadGEM3-GC3.1 global model shows a reduced dry bias over the equatorial Pacific in its configuration with a 1/12° ocean and a 50-km atmosphere (Roberts et al., 2019). By contrast, the eddy-rich MPI-ESM1.2-ER global model (1/12° ocean as well) shows no evident changes in equatorial precipitation when coupled to a 100-km atmosphere (Gutjahr et al., 2019). Combined, these results suggest that resolutions finer than 25–50 km might be needed in both the atmosphere and ocean to improve surface coupling and reduce biases. However, minimizing equatorial precipitation biases might actually be much more complex than simply increasing model resolution, as found for the ICON global atmosphere-ocean model with a uniform grid spacing of 5 km. Despite its high atmosphere and ocean resolutions, this model still exhibits a strong dry bias over the equatorial Pacific driven by a surface cold bias underneath (Hohenegger et al., 2022; Segura et al., 2022). This model, however, is not directly comparable to those other HighResMIP models, as it includes a minimum set of parametrization. Thus, while convection is directly resolved in ICON, it is parametrized in VHR and the listed models. The incorrect representation of the equatorial SST structure in ICON might instead be related to unresolved sub-grid processes (Segura et al., 2022).

The Gulf Stream is another region in which increased model resolution is beneficial, with a reduced temperature biases over the separation region and the central North Atlantic in VHR compared to HR and LR. Such improvements have been related to the resolving of the first baroclinic Rossby radius of deformation over most of the region and/or the exceeding of a critical Reynolds number (e.g., Chassignet and Marshall, 2008). Similar results have also been reported for the HadGEM3-GC3.1 (Roberts et al., 2019) and MPI-ESM1.2-ER (Gutjahr et al., 2019) global models, both with a 1/12° oceanic grid but coarser atmospheric grids (~50 km and ~100 km, respectively). This suggests that oceanic resolution is a critical factor for the Gulf
<figure><img src="image_80.png" /></figure>

<figure><img src="image_81.png" /></figure>

624 Stream representation. Nonetheless, other model features might also be relevant to simulate a
625 realistic Gulf Stream, as no improvement is found in the CESM1.3 model between a 1°- and a
626 0.1°- oceanic grid, for which the Gulf Stream separation occurs too far north (Chang et al.,
627 2020). One of the many potential reasons behind the discrepancy might be the obvious difference
628 in the number of atmospheric vertical levels: 91 in VHR, 85 in HadGEM3-GC3.1 (Roberts et al.,
629 2019), 95 in MPI-ESM1.2-ER (Gutjahr et al., 2019), but only 30 in CESM1.3 (Meehl et al.,
630 2019), which is expected to degrade the representation of key stratosphere-troposphere
631 interactions affecting North Atlantic variability, and, by extension, the wind field, which is
632 critical for the Gulf Stream separation. As nicely summarized in Chassignet and Marshall (2008),
633 however: “The Gulf Stream separation, indeed, turns out to be quite sensitive to a variety of
634 other factors such as subgrid scale parametrization, subpolar gyre strength and water mass
635 properties, [deep western boundary current] strength, representation of topography, and the
636 choice of model grid”. A realistic representation of the Gulf Stream is crucial for the North
637 Atlantic and European climate. SST biases in the Gulf Stream can drive not only local changes
638 over the North Atlantic, but a large-scale dynamic response over remote regions of the Northern
639 Hemisphere through a quasi-zonal planetary barotropic Rossby wave response (Lee et al., 2018).
640 Similarly, a more realistic, farther-south Gulf Stream has been shown to shift north in
641 simulations with increased CO₂ in models at eddy-rich resolutions (Saba et al., 2016;
642 Moreno-Chamarro et al., 2021). This shift would lead to amplified warming of the US East
643 coastal region, which might be consistent with the anomalous warming observed in the Gulf
644 Stream area in recent decades (Pershing et al., 2015; Todd and Ren, 2023). Reducing biases in
645 the Gulf Stream area is therefore key to reproducing a realistic atmospheric circulation and to the
646 sensitivity of the response to an external forcing.

647 Mainly related to increased atmospheric resolution, VHR also shows reduced precipitation
648 biases over mountain ranges all over the world. This suggests VHR might provide more realistic
649 regional information of precipitation variability and future changes than lower resolution models
650 can. Giorgi et al. (2016), in fact, showed that increased model resolution leads to stronger
651 summer precipitation changes over the Alpine region, using climate change projections with a
652 regional atmospheric model of ~12-km grid. VHR uses a similar resolution but on a global scale,
653 without the need to be constrained by lower resolution models.
<figure><img src="image_82.png" /></figure>

654 On the negative side, we find that increased model resolution alone can be insufficient to reduce important and well-known biases in the climate or even cause model degradation in VHR.

655 The warm bias over the coastal tropical upwelling areas, the Southern Ocean warm bias, and the rainfall excess bias over warm tropical waters all persist or even increase in VHR compared to HR and LR. These biases point to deficiencies in the model physics, specially in the atmosphere, and more particularly, in the cloud parameterizations. In VHR, both the warm bias over eastern tropical upwelling areas and the Southern Ocean are connected to negative biases in cloud cover.

661 This reinforces the established idea that insufficient stratocumulus decks over the upwelling areas (e.g., Richter, 2015) and mixed-phase clouds over the Southern Ocean (e.g., Hyder et al., 2018) play key roles in setting up those biases. Cloud biases can be particularly insensitive to increases in model resolution, both in the ocean and atmosphere, from ~100-km grids to 25–50-km grids (Moreno-Chamarro et al., 2022). Yet, for example, improved cloud microphysics closer to observations have been shown to help reduce shortwave radiation biases over the Southern Ocean in the Met Office's Unified Model (Varma et al., 2020). Reducing these biases as much as possible is critical, since they can have wider, global impacts on the climate, driving, for example, additional biases in tropical precipitation through the effect on the global energy budget (e.g., Hwang et al., 2013; Hawcroft et al., 2017).

671 It is interesting to note, nonetheless, that although LR, HR, and VHR all share the same cloud scheme, it is VHR that develops the strongest Southern Ocean bias. This might be related to the lack of additional model tuning from LR to HR and VHR. Rackow et al. (2024) showed that tuning the top-of-the-atmosphere radiation contributed to reducing the warming excess over the Southern Ocean in the IFS-FESOM global model at ~5-km resolution. The HighResMIP protocol suggests that no tuning is performed across resolutions to ensure any changes in the simulated climate can solely be attributed to changes in resolution (Haarsma et al., 2016). This approach can lead to undesired model degradation: for example, the untuned, low-resolution ECMWF model for HighResMIP shows an overly weak AMOC and a large cold bias over the North Atlantic compared to its well-tuned, high-resolution counterpart (Roberts C.D. et al., 2018). This can hinder model comparison and a clean understanding of the effect of model resolution, as biases can have large-scale climatic impacts (e.g., Hwang et al., 2013; Hawcroft et al., 2017; Lee et al., 2018) and affect the response sensitivity to forcing (e.g., McGee et al., 2018).
<figure><img src="image_83.png" /></figure>

<figure><img src="image_84.png" /></figure>

With respect to the spin-up, the HighResMIP protocol suggests a 50-year period (Haarsma et al., 2016). For all the configurations, this period is insufficient to equilibrate the full ocean, although the upper 1000 m equilibrates faster than the lower-part, and VHR does it faster and appears more stable after 100 years than HR and LR. The eddy-rich HadGEM3-GC3.1 also shows smaller drifts at the end of the 50-year period than its lowest resolution versions (Roberts et al., 2019). By contrast, for the CESM1.3 model, the low and high-resolution configurations only show a more stable climate after 150 years, related to a strong top-of-the-atmosphere energy imbalance (Chang et al., 2020). This led the authors to propose “150 to 200 years of model spin-up as a future strategy for initializing HR climate model simulations” (Chang et al., 2020). However, considering how computationally expensive these simulations are, new techniques might need to be introduced to tune and spin these models up faster and for longer. As much as tuning can still be “artisanal in character” at many research centers (Mauritsen et al., 2012), new and faster methods are being implemented to speed up the exploration of the space of parameters to find the best fit with observations. These methods include for example machine learning (Hourdin et al., 2021), simplified configurations (Wan et al., 2014), adjoints (Lyu et al., 2018), or model emulators (Williamson et al., 2013). Additional techniques have also been proposed to spin models up faster at much less computational costs; these include using for example Newton-Krylov methods (Bernsen et al., 2008; Merlis and Khatiwala, 2008), or replacing the atmosphere model by model data (Lofverstrom et al., 2020). Implementing similar techniques in future HR and VHR simulations would help accelerate both the spin-up and tuning phases.

To summarize, we here present the eddy-rich version of the EC-Earth global climate model, EC-Earth3P-VHR, with atmospheric and oceanic resolutions of 10–15 km. The analysis of its main climate features reveals improvements with respect to two lower resolution versions, such as a reduced dry equatorial bias over the Pacific, a more realistic Gulf Stream representation, and more accurate rainfall over mountain areas. Other biases persist or degrade, such as the warm biases over the subtropical upwelling regions and Southern Ocean, or the tropical precipitation excess. VHR’s global resolution is at a similar level of many regional models, such as those participating in CORDEX, and it is much finer than most of the standard CMIP models. This opens a window of opportunity for model comparison and evaluation, as well as process understanding of much more realistic present-day and future climate and on a more regional scale.
<figure><img src="image_85.png" /></figure>

<figure><img src="image_86.png" /></figure>

716

717 Code and Data Availability

718 The data of the EC-Earth3P-LR and -HR models are available from ESGF
719 (https://esgf-index1.ceda.ac.uk/search/cmip6-ceda/, last access: 20 June 2024) via the references
720 provided in Section 2.3: EC-Earth3P (https://doi.org/10.22033/ESGF/CMIP6.4683, EC-Earth,
721 2018; https://doi.org/10.22033/ESGF/CMIP6.4682, EC-Earth, 2019). Data of ERA-5 are freely
722 available at https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5 (Hersbach et al.,
723 2020; https://doi.org/10.24381/cds.6860a573, Hersbach et al., 2019), while GPCP data are at
724 https://psl.noaa.gov/data/gridded/data.gpcp.html (Adler et al., 2003), ESA cloud cover data are at
725 https://climate.esa.int/en/projects/cloud/data/ (Stengel et al., 2020), EN4 data version 4.2.2 are at
726 https://www.metoffice.gov.uk/hadobs/en4/ (Good et al., 2013), OSI SAF (OSI-409/OSI-409-a)
727 sea ice concentration data are at https://osi-saf.eumetsat.int/products/sea-ice-products
728 (EUMETSAT Ocean and Sea Ice Satellite Application Facility, 2015), GIOMAS sea ice volume
729 data are at https://psc.apl.washington.edu/zhang/Global_seaice/data.html (Zhang and Rothrock,
730 2003), and J-OFURO3 flux data are at https://www.j-ofuro.com/en/dataset/ (Tomita et al., 2019).
731 The model data and plot scripts to reproduce the figures can be obtained from
732 https://zenodo.org/records/12078052 (Moreno-Chamarro, 2024). The model code developed at
733 ECMWF, including IFS and the Finite Volume Module (FVM), is intellectual property of
734 ECMWF and its member states. Permission to access the EC-Earth source code can be requested
735 from the EC-Earth community via the EC-Earth website (http://www.ec-earth.org/, last access:
736 July 2024) and may be granted, if a corresponding software license agreement is signed with
737 ECMWF. The repository tag for the version of IFS and EC-Earth3P-VHR used in this work is
738 3.2.2 (see Section 2.1) and is available through r8643. The EC-Earth workflow software used to
739 run the simulations at the BSC is stored and version controlled in the BSC Earth Sciences GitLab
740 repository (https://earth.bsc.es/gitlab/es/auto-ecearth3, last access: July 2024). Permission to
741 access the repository can be requested from the Earth Sciences Department at the BSC and may
742 be granted if the applicant has access to the EC-Earth code and the BSC HPC infrastructure. The
743 workflow management system for running the simulations is distributed under Apache License
744 2.0 as a public project (https://earth.bsc.es/gitlab/es/autosubmit, last access: July 2024) in the
745 BSC GitLab repository.

746
<figure><img src="image_87.png" /></figure>

<figure><img src="image_88.png" /></figure>

747 Author Contributions

748 TA, MA, MC, EF, and SP developed the model setup. EMC and TA ran the simulations. PAB

749 and DK post-processed and cmorized the model data. EMC analyzed the data and wrote the

750 manuscript with input from all the authors.

751

752 Competing interests

753 The authors declare that they have no conflict of interest.

754

755 Acknowledgements

756 This research has been supported by the Horizon2020 PRIMAVERA project (H2020 GA

757 641727). EMC acknowledges funding from the Spanish Science and Innovation Ministry

758 (Ministerio de Ciencia e Innovación) via the STREAM project (PID2020-114746GB-I00). MA

759 has received funding from the National Research Agency through OEMES

760 (PID2020-116324RA-I00). This work has received funding from the European High

761 Performance Computing Joint Undertaking (JU) under the ESiWACE CoE, grant agreement No

762 101093054.

763

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1007 Rackow, T., Pedruzo-Bagazgoitia, X., Becker, T., Milinski, S., Sandu, I., Aguridan, R., Bechtold, P., Beyer, S., Bidlot, J., Boussetta, S., Diamantakis, M., Dueben, P., Dutra, E., Forbes, R., Goessling, H. F., Hadade, I., Hegewald, J., Keeley, S., Kluft, L., Koldunov, N., Koldunov, A., Kölling, T., Kousal, J., Mogensen, K., Quintino, T., Polichtchouk, I., Sármány, D., Sidorenko, D., Streffing, J., Sützl, B., Takasuka, D., Tietsche, S., Valentini, M., Vannière, B., Wedi, N., Zampieri, L., and Ziemen, F.: Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5/NEMOv3.4, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-913, 2024.

1015 Renault, L., Lemarié, F. and Arsouze, T.: On the implementation and consequences of the oceanic currents feedback in ocean–atmosphere coupled models. Ocean Modelling, 141, 101423, https://doi.org/10.1016/j.ocemod.2019.101423, 2019.

1018 Renault, L., Marchesiello, P., & Contreras, M.: Coaction of top and bottom drags in Gulf Stream dynamics. Journal of Geophysical Research: Oceans, 128, e2022JC018939. https://doi.org/10.1029/2022JC018939, 2023.
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1021 Richter, I.: Climate model biases in the eastern tropical oceans: Causes, impacts and ways forward, *Wires Clim. Change*, 6, 345–358, https://doi.org/10.1002/wcc.338, 2015.

1023 Roberts, C. D., Senan, R., Molteni, F., Boussetta, S., Mayer, M., and Keeley, S. P. E.: Climate model configurations of the ECMWF Integrated Forecasting System (ECMWF-IFS cycle 43r1) for HighResMIP, Geosci. Model Dev., 11, 3681–3712, https://doi.org/10.5194/gmd-11-3681-2018, 2018.

1027 Roberts, M. J., Baker, A., Blockley, E. W., Calvert, D., Coward, A., Hewitt, H. T., Jackson, L. C., Kuhlbrodt, T., Mathiot, P., Roberts, C. D. and Schiemann, R.: Description of the resolution hierarchy of the global coupled HadGEM3-GC3. 1 model as used in CMIP6 HighResMIP experiments. *Geoscientific Model Development*, 12(12), 4999–5028, https://doi.org/10.5194/gmd-12-4999-2019, 2019.

1032 Roberts, M. J., Camp, J., Seddon, J., Vidale, P. L., Hodges, K., Vanniere, B., Mecking, J., Haarsma, R., Bellucci, A., Scoccimarro, E., and Caron, L. P.: Impact of model resolution on tropical cyclone simulation using the HighResMIP-PRIMAVERA multimodel ensemble, J. Climate, 33, 2557–2583, https://doi.org/10.1175/JCLI-D-19-0639.1, 2020a.

1036 Roberts, M. J., Jackson, L. C., Roberts, C. D., Meccia, V., Docquier, D., Koenigk, T., Ortega, P., Moreno-Chamarro, E., Bellucci, A., Coward, A., and Drijfhout, S.: Sensitivity of the Atlantic meridional overturning circulation to model resolution in CMIP6 HighResMIP simulations and implications for future changes, J. Adv. Model. Earth Sy., 12, e2019MS002014, https://doi.org/10.1029/2019MS002014, 2020b.

1041 Roberts, M. J., Vidale, P. L., Senior, C., Hewitt, H. T., Bates, C., Berthou, S., Chang, P., Christensen, H. M., Danilov, S., Demory, M. E., and Griffies, S. M.: The benefits of global high resolution for climate simulation: process understanding and the enabling of stakeholder decisions at the regional scale, B. Am. Meteorol. Soc., 99, 2341–2359, https://doi.org/10.1175/BAMS-D-15-00320.1, 2018.

1046 Saba, V.S., Griffies, S.M., Anderson, W.G., Winton, M., Alexander, M.A., Delworth, T.L., Hare, J.A., Harrison, M.J., Rosati, A., Vecchi, G.A. and Zhang, R.: Enhanced warming of the Northwest Atlantic Ocean under climate change. *Journal of Geophysical Research: Oceans*, 121(1), 118–132, https://doi.org/10.1002/2015JC011346, 2016.

1050 Segura, H., Hohenegger, C., Wengel, C. and Stevens, B.: Learning by doing: Seasonal and diurnal features of tropical precipitation in a global-coupled storm-resolving model.
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1054 Small, R. J., Bryan, F. O., Bishop, S. P. and Tomas, R. A.: Air-sea turbulent heat fluxes in climate models and observational analyses: What drives their variability?. Journal of Climate, 32(8), 2397–2421, https://doi.org/10.1175/JCLI-D-18-0576.1, 2019.

1057 Stengel, M., Stapelberg, S., Sus, O., Finkensieper, S., Würzler, B., Philipp, D., Hollmann, R., Poulsen, C., Christensen, M., and McGarragh, G.: Cloud_cci Advanced Very High Resolution Radiometer post meridiem (AVHRR-PM) dataset version 3: 35 year climatology of global cloud and radiation properties, Earth Syst. Sci. Data, 12, 41–60, https://doi.org/10.5194/essd-12-41-2020, 2020 (data available at: https://climate.esa.int/en/projects/cloud/data/, last access: 10 March 2021).

1063 Sun, X. and Wu, R.: Spatial scale dependence of the relationship between turbulent surface heat flux and SST. Climate Dynamics, 58(3), 1127–1145, https://doi.org/10.1007/s00382-021-05957-9, 2022.

1066 Tian, B. and Dong, X.: The double-ITCZ bias in CMIP3, CMIP5, and CMIP6 models based on annual mean precipitation, Geophys. Res. Lett., 47, e2020GL087232, https://doi.org/10.1029/2020GL087232, 2020.

1069 Tintó Prims, O., M. C. Acosta, A. M. Moore, M. Castrillo, K. Serradell, A. Cortés and F. J. Doblas-Reyes: How to use mixed precision in ocean models: exploring a potential reduction of numerical precision in NEMO 4.0 and ROMS 3.6. Geoscientific Model Development, 12, 3135–3148, https://doi.org/10.5194/gmd-12-3135-2019, 2019a.

1073 Tintó Prims O., M. Castrillo, M. C. Acosta, O. Mula-Valls, A. Sanchez Lorente, K. Serradell, A. Cortés and F. J. Doblas-Reyes: Finding, analysing and solving MPI communication bottlenecks in Earth System models. Journal of Computational Sciences, 36, 100864, https://doi.org/10.1016/j.jocs.2018.04.015, 2019b.

1077 Todd, R. E. and Ren, A. S.: Warming and lateral shift of the Gulf Stream from in situ observations since 2001. Nature Climate Change, 13(12), 1348–1352, https://doi.org/10.1038/s41558-023-01835-w, 2023.

1080 Tomita, H., Hihara, T., Kako, S.I., Kubota, M. and Kutsuwada, K.: An introduction to J-OFURO3, a third-generation Japanese ocean flux data set using remote-sensing observations. Journal of Oceanography, 75(2), 171–194, 2021.
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1092 Vancoppenolle, M., Bouillon, S., Fichefet, T., Goosse, H., Lecomte, O., Morales Maqueda, M. A., and Madec, G.: The Louvain-la-Neuve sea ice model, Notes du pole de modélisation, Institut Pierre-Simon Laplace (IPSL), Paris, France, No. 31, 2012.

1095 Varma, V., Morgenstern, O., Field, P., Furtado, K., Williams, J., and Hyder, P.: Improving the Southern Ocean cloud albedo biases in a general circulation model, Atmos. Chem. Phys., 20, 7741–7751, https://doi.org/10.5194/acp-20-7741-2020, 2020.

1098 Vidale, P. L., Hodges, K., Vannière, B., Davini, P., Roberts, M. J., Strommen, K., Weisheimer, A., Plesca, E., and Corti, S.: Impact of stochastic physics and model resolution on the simulation of Tropical Cyclones in climate GCMs, J. Climate, 34, 4315–4341, https://doi.org/10.1175/JCLI-D-20-0507.1, 2021.

1102 Wan, H., Rasch, P. J., Zhang, K., Qian, Y., Yan, H. and Zhao, C.: Short ensembles: An efficient method for discerning climate-relevant sensitivities in atmospheric general circulation models. Geoscientific Model Development, 7(5), 1961–1977, https://doi.org/10.5194/gmd-7-1961-2014, 2014.

1106 Wengel, C., Lee, S. S., Stuecker, M. F., Timmermann, A., Chu, J. E., and Schloesser, F.: Future high-resolution El Niño/Southern Oscillation dynamics, Nat. Clim. Change, 1–8, https://doi.org/10.1038/s41558-021-01132-4, 2021.

1109 Williamson, D., Goldstein, M., Allison, L., Blaker, A., Challenor, P., Jackson, L. and Yamazaki, K.: History matching for exploring and reducing climate model parameter space using observations and a large perturbed physics ensemble. Climate Dynamics, 41, 1703–1729, https://doi.org/10.1007/s00382-013-1896-4, 2013.
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1113 Woollings, T., Hoskins, B., Blackburn, M., Hassell, D. and Hodges, K.: Storm track sensitivity to sea surface temperature resolution in a regional atmosphere model. Climate Dynamics, 35, 341–353, https://doi.org/10.1007/s00382-009-0554-3, 2010.

1116 Xepes-Arbós, X., G. van den Oord, M. C. Acosta and G. D. Carver: Evaluation and optimisation of the I/O scalability for the next generation of Earth system models: IFS CY43R3 and XIOS 2.0 integration as a case study. Geoscientific Model Development, 15, 379–394, https://doi.org/10.5194/gmd-15-379-2022, 2022.

1120 Zhang, J. and Rothrock, D. A.: Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates, Mon. Weather Rev., 131, 845–861, https://doi.org/10.1175/1520-0493(2003)131<0845:Mgsiwa>2.0.Co;2, 2003 (data available at: https://psc.apl.washington.edu/zhang/Global_seaice/model.html, last access: 6 March 2019).

1125 Zhang, W., Villarini, G., Scoccimarro, E., Roberts, M., Vidale, P. L., Vanniere, B., Caron, L. P., Putrasahan, D., Roberts, C., Senan, R., and Moine, M. P.: Tropical cyclone precipitation in the HighResMIP atmosphere-only experiments of the PRIMAVERA Project, Clim. Dynam., 57, 253–273, https://doi.org/10.1007/s00382-021-05707-x, 2021.