| 23 Jun 2026
The Madden Julian Oscillation in high-resolution coupled climate simulations: mean state evaluation and historical variability in IFS-NEMO
Abstract. This work examines the representation of the MJO in two high-resolution versions of the fully coupled General Circulation Model IFS-NEMO, a new-generation climate model developed at the BSC within the European Project EERIE and the Destination Earth initiative. We analyse two historical HighResMIP simulations of IFS-NEMO performed at two different horizontal resolutions: 9 km and 25 km, to investigate the impact of the resolution on MJO performance. The model correctly reproduces the main dynamical features associated with the MJO when defined via multivariate EOF analysis, and exhibits typical model biases. The model correctly reproduces the spatial properties of the two leading observed EOFs, but the mean amplitude of the MJO and the intensity of the convective signal are generally underestimated, with a reduction of spectral power of about 20 % at both resolutions with respect to satellite observations. IFS-NEMO also exhibits a reduced eastward propagation of the intraseasonal precipitation and convective signals associated with the MJO. This is explained by the strong dry bias found in the early phases of the MJO (over the Indian Ocean) in both versions of IFS-NEMO with respect to ERA5. Low-atmospheric moistening is crucial during those phases as moisture accumulation drives both vertical and horizontal humidity advection. Overall, the 10 Km configuration seems to improve the structure of the two leading EOFs and their explained variance, without solving the other biases. The simulations do not reproduce the long-term change of MJO activity observed over the full historical period. In particular, the MJO variance increases in observations, while none of the IFS-NEMO simulations shows a significant trend. This is due to cold ocean and background circulation biases. Finally, the implications that such a discrepancy can have on predictability are discussed by analysing the changes in weighted permutation entropy of the MJO amplitude time series. Contrary to ERA5, the predictability does not increase in IFS-NEMO at both resolutions, as the model fails to reproduce the externally forced predictive component of the MJO, thus questioning their suitability to investigate the projected future evolution.
The manuscript reports on the MJO simulation by two versions of the IFS-NEMO model configured at two horizontal resolutions: 9 km and 25 km. The manuscript is well written. The diagnostics applied to evaluate the MJO characteristics simulated by the model are described in detail, which makes the analysis easy to follow. There are only a few minor changes that need to be implemented:
L37: Maloney and Hartmann (2001) was the first study investigating the relationship between MJO and TCs.
L175: Waliser (2009) applies a 20-100 day filter whereas Kim et al. (2025) apply a 20-70 filter. Since one of the objectives of the paper is to also conduct sensitivity testes (e.g., OLR from NOAA satellite data set and ERA 5, reconstruction of anomalies, propagation speed), the authors should also consider evaluating the sensitivity of analysis to the filtering window.
L211: Something is missing after 96. Perhaps days? In parenthesis: 35=years, 365=days, Explain the meaning of 12.
L212: Every step in the analysis is well justified, except the choice of dof=1000. How was this value selected? Were sensitivity tests conducted?
L265: The variance explained is shown in Table 1.
L321: Panels in Figure 4 show no dashed lined marking the MJO domain in the wavenumber frequency space.
Moist static energy (MSE) is a metric used to characterize the MJO amplitude. An elegant diagnostic explaining the amplitude of the MJO is the charged MSE introduced by Choi et al. (2026). This diagnostic would make a nice addition to the evaluation.
L573-574: Results from simulation using the super-parameterization confirmed that cloud -permitting schemes for moist convection alleviate this bias (e.g., Thayer-Calder and Randall, 2009; Stan et al., 2010)
Figure 8: Add labels to every panel and include a reference to the specific panel in the text when discussing them.
The variances shown in Figure 8 are consistent with the power spectrum in Figure 4. It would be interesting to compare the spectra for the earlier period when there is a better agreement between ERA5 and the models. That comparison can be added to the supplemental materials.
Maloney, E. D., and D. L. Hartmann, 2001: The Madden–Julian Oscillation, Barotropic Dynamics, and North Pacific Tropical Cyclone Formation. Part I: Observations. J. Atmos. Sci., 58, 2545–2558, https://doi.org/10.1175/1520-0469(2001)058<2545:TMJOBD>2.0.CO;2.
Choi, N., J. Stanczak, and C. Stan, 2026: The Role of Atmosphere–Ocean Coupling on the Prediction of Madden–Julian Oscillation. J. Climate, 39, 3669–3684, https://doi.org/10.1175/JCLI-D-25-0247.1.
Thayer-Calder, K., and D. A. Randall, 2009: The Role of Convective Moistening in the Madden–Julian Oscillation. J. Atmos. Sci., 66, 3297–3312, https://doi.org/10.1175/2009JAS3081.1.
Stan, C., Khairoutdinov, M., C.A. DeMott, V. Krishnamurthy, D.Straus, D.A Randall, J. L. Kinter, and J Shukla, 2010: An ocean-atmosphere climate simulation with an embedded cloud resolving model. Geophysical Research Letters, 37, https://doi.org/10.1029/2009GL040822.