The highresSST-present simulations will be compared with ERA5 (Hersbach et al., 2020) (1979–2014), except for precipitation, where Global Precipitation Climatology Project v2.3 (1979–2014) (Adler et al., 2003) data will be used. EC-Earth, GPCP and ERA5 data are regridded to a common grid (N128) before comparison. Seasonal means (December–February (DJF) and June–August (JJA)) will be analyzed. Ensemble mean fields will be displayed.

Due to the prescribed SST, the largest surface air temperature (SAT) biases are over the continents (Fig. 6). The largest negative biases are over central Africa for DJF and Alaska in JJA, while the largest positive biases are located over Antarctica in JJA and northeastern Siberia in DJF. Over most areas, EC-Earth3P-HR is slightly too cold. Over most of the tropics, the mean sea level pressure (MSLP) is underestimated, whereas over Antarctica and the surrounding regions of the Southern Ocean it has a strong positive bias (Fig. 7). Also noteworthy is the positive bias south of Greenland during DJF. The largest precipitation errors are seen in the tropics over the warm pool regions in the Pacific and the Atlantic with too much precipitation (Fig. 8). The planetary wave structure of the geopotential height at 500 hPa (Z500) during DJF is well represented, with the exception of the region south of Greenland (Fig. 9), which is consistent with the MSLP bias (Fig. 7a). The physical causes of the aforementioned biases can include a wide range of deficiencies in the parameterizations of cloud physics, land surface and snow, to mention a few. In forthcoming papers, this will be investigated in further detail.

Doubling of the atmospheric horizontal resolution has only a modest impact on the large-scale structures of the main meteorological variables, as illustrated by the global MSLP, SAT and precipitation (Fig. 10). For SAT, the differences are generally less than 1 K; for MSLP, they are 1 hPa, except in the polar regions. A remarkable result is the worsening of the bias over Antarctica during JJA. Because the dynamics of the polar vortex, which is sensitive to horizontal resolution, is strongest during austral winter, we speculate that this enhanced bias is associated with it. The exact mechanism falls outside the scope of this basic validation and will be explored in forthcoming studies. For precipitation, the difference can be larger than 1.5 mm d-1 in the tropics. It is possible to conclude that the increase of resolution does not have a clear positive impact on the climatology of any of those variables. For instance, for precipitation, it results in an increase of the wet bias over the warm pool (compare with Fig. 8). Also measured by the root mean square error (RMSE) (see figure captions for the numbers), the impact of resolution is small, on the order of 10 % or less depending on the variable and the season. Enhancing resolution reduces the RMSE for SAT and MSLP, whereas it slightly increases for precipitation (from 1.04 to 1.06 in DJF and from 1.35 to 1.44 in JJA).

As discussed in the outline of the HighResMIP protocol, the spin-up was started from an initial state that is based on observations for 1950. For the ocean, this is the EN4 ocean reanalysis (Good et al., 2013) averaged over the 1950–1954 period, with 3 m sea-ice thickness in the Arctic and 1 m in the Antarctic. The atmosphere–land system was initialized from ERA-20C for 1 January 1950 and spun up for 20 years to let the soil moisture reach equilibrium. For the ocean, no data assimilation has been performed, which can result in imbalances between the density and velocity fields, giving rise to initial shocks and waves.

During the first years of the spin-up, there is a strong drift in the model climate (not shown). For the fast components of the climate system like the atmosphere and the mixed layer of the ocean, the adjustment is on the order of 1 year, whereas the slow components such as the deep ocean require a thousand years or more to reach equilibrium. For the land component, this is on the order of a decade. As a consequence, after a spin-up of 50 years, the atmosphere, land and upper ocean are approximately in equilibrium, while the deeper ocean is still drifting. The largest drift occurs in the layer of 100–1000 m with a drift of 0.5 ∘C per century. This drift also has an impact on the fast components of the climate system, which therefore still might reveal trends.

After the spin-up, the SAT each of the three members of EC-Earth3P-HR is in quasi-equilibrium and the global mean temperature oscillates around 13.9 ∘C (Fig. 11a, black). The ocean is still warming, as expressed by a negative net surface heat flux on the order of -1.5 W m-2 (positive is upward) (Fig. 11b, black). This imbalance is reduced during the simulation but without an indication that the model is getting close to its equilibrium state.

Contrary to EC-Earth3P-HR, the global annual mean SAT of EC-Earth3P displays a significant upward trend, with an indication of stabilizing after about 35 years (Fig. 11a, red). This warming trend is caused by a large warming of the North Atlantic, as revealed in Fig. 12, showing the difference between the first and last 10 years of the control-1950 run. This warming is caused by the activation of the deep convection in the Labrador Sea (not shown) that started about 10 years after the beginning of the control simulation, which was absent in the spin-up run. Associated with that, the AMOC also shows an upward trend (see Fig. 17 below). This switch to a warmer state does not strongly affect the slow warming of the deeper ocean, which is reflected in a similar behavior of the net surface heat flux to that for EC-Earth3P-HR (Fig. 11b). The reasons for the initial absence of deep convection in the Labrador Sea in EC-Earth3P and the difference with EC-Earth3P-HR are not clear and presently under investigation. Possible candidates are that the differences in ocean resolution affect the sea-ice dynamics and deep convection, but also changes in ocean temperature and salinity distribution may play a role.

The control-1950 experiment is also analyzed to evaluate model performance of internally generated variability in the coupled system; the targets are the El Niño–Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), sudden stratospheric warmings (SSWs) and the Atlantic meridional overturning circulation (AMOC).

Figure 13 depicts the seasonal cycle of the NINO3.4 index (SST anomalies averaged over 5∘ S–5∘ N, 170–120∘ W). As it was also shown for EC-Earth3.1 (Yang et al., 2019), both EC-Earth3P and EC-Earth3P-HR still have a systematic underestimation of the ENSO amplitude from late autumn to mid-winter and yield the minimum in July, 1–2 months later than in observations. Increasing model resolution reduces the bias in early summer (May–June) but worsens it in late summer (July–August). Overall, EC-Earth3P-HR shows lower ENSO variability than EC-Earth3P, which following Yang et al.'s (2019) arguments suggests that the ocean–atmosphere coupling strength over the tropical Pacific is weaker in the high-resolution version of the model. On the other hand, Fig. 14 displays the spatial distribution of winter SST variability and the canonical ENSO pattern, computed as linear regression onto the NINO3.4 index. Increasing model resolution leads to a reduction in the unrealistic zonal extension of the cold tongue towards the western tropical Pacific, which was also present in EC-Earth3.1 (Yang et al., 2019) and is a common bias in climate models (e.g., Guilyardi et al., 2009): EC-Earth3P reaches longitudes of Papua New Guinea (Fig. 14a), while EC-Earth3P-HR improves its location (Fig. 14b), yet overestimates it compared to observations (Fig. 14c). Note that the reduction of this model bias is statistically significant (Fig. 14g). Consistently, the improvement in the cold tongue translates into a better representation of the ENSO pattern (Fig. 14d–f). Nonetheless, the width of the cold tongue in EC-Earth3P-HR is still too narrow in the central tropical Pacific (see also Yang et al., 2019), which again is a common bias in climate models (e.g., Zhang and Jin, 2012). Both EC-Earth3P and EC-Earth3P-HR realistically simulate the wave-like structure of the ENSO teleconnection in the extratropics (Fig. 14d–f).

On another matter, note that EC-Earth3P-HR (Fig. 14b) captures the small-scale features and meanderings along the western boundary currents, Kuroshio–Oyashio and Gulf Stream, and the sea-ice edge over the Labrador Sea much better than EC-Earth3P (Fig. 14a). In these three areas, there is a substantial increase in SST variability (Fig. 14g), which following Haarsma et al. (2019) is likely due to increasing ocean resolution rather than atmosphere resolution.

Figure 15 illustrates how EC-Earth3P(-HR) simulates the surface NAO and its hemispheric signature in the middle troposphere. The NAO (here measured as leading empirical orthogonal function (EOF) of the DJF SLP anomalies over 20–90∘ N, 90∘ W–40∘ E) accounts for virtually the same fraction of SLP variance in both model versions, i.e., 42.70 % in EC-Earth3P (Fig. 15d) and 42.74 % in EC-Earth3P-HR (Fig. 15e), and still slightly underestimates the observed one (∼ 50 % in ERA-Interim, Fig. 15f); the same applied to EC-Earth2.2 when compared to ERA-40 (Hazeleger et al., 2012). EC-Earth rightly captures the circumglobal pattern associated with the NAO at upper levels (e.g., Branstator, 2002; García-Serrano and Haarsma, 2017), particularly the elongated lobe over the North Atlantic and the two centers of action over the North Pacific (Fig. 15a–c). A close inspection to the barotropic structure of the NAO reveals that the meridional dipole is shifted westward in EC-Earth3P-HR (Fig. 15b, e) as compared to EC-Earth3P (Fig. 15a, d), which according to Haarsma et al. (2019) could be related to increasing ocean resolution and a stronger forcing of the North Atlantic storm track.

Also, the simulation of SSW occurrence is assessed (Fig. 16); the identification follows the criterion in Palmeiro et al. (2015). The decadal frequency of SSWs in EC-Earth is about eight events per decade regardless of model resolution, which is underestimated when compared to ERA-Interim (∼ 11 events per decade) but in the range of observational uncertainty (e.g., Palmeiro et al., 2015; Ayarzagüena et al., 2019). The same underestimation was diagnosed in EC-Earth3.1 (Palmeiro et al., 2020a). The reduced amount of SSWs is probably associated with a too-strong bias at the core of the polar vortex, still present in EC-Earth3.3 (Palmeiro et al., 2020b). It is thus concluded that increasing horizontal resolution does not affect the model bias in the strength of the polar vortex. The seasonal cycle of SSWs in reanalysis is quite robust over the satellite period, showing one maximum in December–January and another one in February–March (Ayarzagüena et al., 2019), which was properly captured by EC-Earth3.1 in the control, coupled simulations with fixed radiative forcing at the year 2000 (Palmeiro et al., 2020a). Here, in control-1950, EC-Earth does not reproduce such a bimodal cycle, with EC-Earth3P-HR (blue) yielding a peak in January–February and EC-Earth3P (red) two relative maxima in January and March. Interestingly, the seasonal cycle of SSWs over the historical, pre-satellite period shows a different distribution with a prominent maximum in mid-winter and a secondary peak in late winter, although it is less robust among reanalysis products (Ayarzagüena et al., 2019). The impact of the radiative forcing on SSW occurrence deserves further research.

The AMOC index was computed as the maximum stream function at 26.5∘ N and between 900 and 1200 m depth. The annual AMOC index of EC-Earth3P-HR for the control-1950 runs (Fig. 17a, black) is about 15 Sv, which is lower than the values form the RAPID array (Smeed et al., 2019) that have been measured since 2004 (stars in Fig. 17b). It reveals interannual and decadal variability, without an evident trend. As already discussed at the beginning of Sect. 4.2.3, the AMOC of EC-Earth3P shows an upward trend (Fig. 17a, red) associated with the activation of convection in the Labrador Sea.

The hist-1950 ensemble simulations differ from the control-1950 simulations in terms of the historical GHG and aerosol concentrations. The global mean annual temperature in EC-Earth3P-HR displays an increase similar to the ERA5 data set (Fig. 18a). The warming seems to be slightly larger in the model. We remind the reader, however, of the enhanced observed warming after 2014, which might result in a similar trend in the model simulations compared to observations up to present day. The cooling due to the Mt. Pinatubo eruption in 1991 is clearly visible in all members and the ensemble mean. The amplitude and period compare well with ERA5. On its part, the AMOC in EC-Earth3P-HR reveals a clear downward trend in particular from the 1990s onward (Fig. 17b, black). This is consistent with a slowdown of the Atlantic overturning due to global warming in CMIP5 models (Cheng et al., 2013).

Similarly to control-1950, the hist-1950 simulations with EC-Earth3P show an upward drift in SAT (Fig. 18a, red) and AMOC (Fig. 17b, red) that are smaller (SAT) or absent (AMOC) in EC-Earth3P-HR. The HighResMIP protocol (Haarsma et al., 2016) of having a control and a historical simulation starting from the same initial conditions was designed to minimize the consequences of such trends. Under the assumption that the model trend is similar for both simulations, it can be eliminated by subtracting the control from the historical simulation. Indeed, the global annual mean SAT and the AMOC of hist-1950 minus control-1950 display a very similar behavior in EC-Earth3P and EC-Earth3P-HR (Figs. 18b and 17c), with an upward trend for SAT and a downward trend for the AMOC. For SAT, the upward trend compares well with ERA5.

Another way to test the representation of the midlatitude atmospheric flow, with a focus on the low-frequency variability (5–30 d), is to assess how well the models reproduce the winter (DJF) Euro-Atlantic weather regimes (Corti et al., 1999; Dawson et al., 2012).

The analysis has been applied here to the EC-Earth3P and EC-Earth3P-HR hist-1950 simulations. Following recent works (Dawson and Palmer, 2015; Strommen et al., 2019), we computed the regimes via k-means clustering of daily geopotential height anomalies at 500 hPa over 30–85∘ N, 80∘ W–40∘ E. As a reference, we considered the ECMWF reanalysis data from ERA40 (1957–1978) and ERA-Interim (1979–2014). The clustering is performed in the space spanned by the first four principal components obtained from the reference data set. More details on the technique used and on the metrics discussed here can be found in Fabiano et al. (2020) and references therein. Each row in Fig. 19 shows the resulting mean patterns of the four standard regimes – NAO+, Scandinavian blocking, Atlantic Ridge and NAO- – for ERA (top), EC-Earth3P (middle) and EC-Earth3P-HR (bottom). The regimes are quite well represented in both configurations. However, the matching is better in the standard-resolution version both in terms of rms and pattern correlation averaged over all regimes (see Table 3). Only the Scandinavian (Sc) blocking pattern is improved in the HR, whereas the other patterns are degraded. The most significant degradation is seen for the NAO- pattern, which is shifted westward in the HR simulation. The result for EC-Earth3P(-HR) goes in the opposite direction of what has been observed in Fabiano et al. (2020), where most models showed a tendency for improving the regime patterns with increased resolution. Concerning the regime frequencies, both model versions show a tendency to produce less NAO+ cases than the observations and more Atlantic Ridge cases (Fig. 19).

Another quantity of interest is the persistence of the regimes, since models usually are not able to reach the observed persistence of the NAO+/- states (Fabiano et al., 2020). As stated in Table 3, this is also observed for the EC-Earth3P hist-1950 simulations, and the effect of the HR is to increase the persistence of NAO+ but decrease that of NAO-.

Even if the HR is degrading the regime patterns, it produces a small but positive effect on the geometrical structure of the regimes. This is shown by the last two quantities in Table 3: the optimal ratio and the sharpness. The optimal ratio is the ratio between the mean inter-cluster squared distance and the mean intra-cluster variance: the larger the optimal ratio, the more clustered the data. The sharpness is an indicator of the statistical significance of the regime structure in the data set in comparison with a randomly sampled multi-normal distribution (Straus et al., 2007). The closer the value is to 100, the more significant the multimodality of the distribution. The sharpness tends to saturate at 100 for very long simulations, so the values reported in Table 3 are obtained from a bootstrap on 30 years chosen randomly. Both the optimal ratio and the sharpness are too low in the EC-Earth3P simulations, as is usually seen for all models. A significant increase with EC-Earth3P-HR is seen for the optimal ratio, and a smaller (non-significant) one is seen for the sharpness.

The increased resolution simulations have a stronger regime structure and are closer to the observations in this sense. However, the regime patterns are degraded in the HR version and this affects the resulting atmospheric flow. A similar result was obtained by Strommen et al. (2019) for a different version of EC-Earth and two other climate models.