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Both are averaged over the period — Both datasets were averaged over the period — The solid contour lines represent eastward zonal wind and the dashed contour lines represent westward zonal wind. The global distribution and the range values of SST are important characteristics of the mean climate state. The spatial average biases are 1. The model still overestimated the SSTs in the major eastern coastal upwelling regions.

This feature is a systematic error observed in different state-of-the-art models that could be caused by the simulation of weaker-than-observed alongshore winds, which consequently leads to an underrepresentation of the upwelling and alongshore currents e. Nevertheless, the bias was negligible over the north equatorial Pacific and in large parts of the tropical western Atlantic. The model simulated a warmer mean SST over the western and extreme eastern parts of the equatorial Pacific Ocean. This positive bias was most notable in the western part of the Pacific, where it was about 1.

However, for the extreme eastern part of the basin, the model showed a lower bias compared with those of the CMIP5 models. The propagation of the SST anomaly patterns from the eastern to the western parts of the Pacific Ocean that occurs throughout the year was not well captured by the model. The same methodology was used for the tropical Atlantic.

This bias started in the central Atlantic and was higher than the biases of the CMIP5 models shown by the shaded grey area in Fig. However, it should be noted that the CMIP5 models also have a warm bias in the eastern part of the tropical Atlantic, which is a problem discussed in previous studies e. This difference of 0. Such a tendency could indicate that the ocean is still drifting from its initial conditions in the historical simulation. The map shown in d presents the difference between the temperature anomalies of the historical simulation relative to the piControl.

The thick black line represents the zero contours. The meridional overturning circulation MOC plays an important role in transporting heat from the tropics to the higher latitudes in both hemispheres.

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This is particularly important in the North Atlantic, where the Atlantic meridional overturning circulation AMOC has a profound impact on the climate of the surrounding continents see Buckley and Marshall, This shallow upper cell of the AMOC is a common feature of state-of-the-art climate models see Menary et al. In the deep ocean, the model accurately simulated the Antarctic Bottom Water flowing northwards over the Atlantic Ocean floor.

This maximum value is within the This value is also in the range of the maximum volume transport strength simulated by other state-of-the-art CMIP5 models Weaver et al. For comparison, Fig. Units are in sverdrup.

In this section

Modeling results indicate that the AMOC has a multidecadal cycle; however, the power spectrum of its strength time series did not show a multidecadal oscillation not shown. The standard deviation of the detrended maximum AMOC strength time series is 1. The sea ice concentration at the end of the Arctic winter was overestimated in the Atlantic, specifically north of Scandinavia Fig. However, at the end of the Arctic summer, the sea ice concentration was underestimated Fig. At the end of the Antarctic summer, the model showed a significant underestimation of the sea ice concentration Fig.

Such seasonal sea ice concentration variations are likely related to the radiative net bias inherent in the model at high latitudes, which results in the generation of higher sea ice extensions during the winter season in each hemisphere compared with those from the reanalysis dataset and excessive sea ice melting during the summer season in each hemisphere. The concentrations are presented as percentages.

In this section, we evaluate the most prominent global climate variability patterns. This evaluation allows us to understand the ability of the model to correctly simulate atmospheric internal and ocean—atmosphere coupled variabilities in the climate system. There are many methods to evaluate the ENSO variability. The model's leading EOF explains The results are shown as the SST anomalies regressed onto the corresponding normalized PC time series degrees Celsius per standard deviation over the period — The percentages of the variance explained by each EOF are indicated in the titles of the panels.

The contour interval is 0. The horseshoe pattern of negative correlation observed over the Pacific Ocean is also weakly simulated by the model, particularly in the westward equatorial region. The anomalies were obtained by subtracting the monthly means for the entire detrended time series at each grid point. The leading modes of coupled ocean—atmosphere variability over the tropical Atlantic Ocean are the zonal mode, also referred to as the equatorial mode Zebiak, ; Lutz et al. The first is an ENSO-like phenomenon that emerges in the Gulf of Guinea mainly during the boreal summer and has a strong impact on west African precipitation Zebiak, ; Lutz et al.

The second is characterized by a cross-equatorial SST gradient associated with meridional wind stress toward the warmer SST anomalies. The maximal amplitude of the meridional mode occurs during the boreal spring and influences the precipitation in northeast Brazil and west Africa Nobre and Shukla, ; Chang et al. The Atlantic Meridional Mode AMM has an interannual and decadal temporal scale of variability and results from a thermodynamic coupling between wind speed, the sea surface evaporation induced by the wind stress, and the SST, a mechanism known as wind—evaporation—SST feedback WES feedback; Xie and Philander, ; Chang et al.

The AMM pattern simulated by the model is similar to that obtained from observations, regardless of the weaker gradient pole in the South Atlantic.

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Nevertheless, the variance explained by the model The patterns shown in Fig. The results are shown as the SST anomalies regressed onto the corresponding normalized PC time series degrees Celsius per standard deviation and wind stress anomalies regressed onto the corresponding normalized PC time series meters per second per standard deviation over the period — The percentages of the variance explained by each EOF are indicated in the titles of the figures.

The formation of the SACZ has a strong influence on the precipitation over southeast South America and is considered, together with the convection activity over the Amazon Basin, the main component of the South American monsoon system Jones and Carvalho, Chaves and Nobre suggest that the cloud cover resulting from the formation of the SACZ over the ocean tends to block solar radiation, thus leading to cooler SSTs beneath.

Nobre et al. Following Nobre et al. Its noteworthy in Fig. To compute and plot the wavenumber—frequency power spectra the MJO Simulation Diagnostic package was used details in Waliser et al. This observed peak has more energy for wavenumber 2.

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A westward-propagating disturbance negative frequencies with weaker energy than the eastward-propagating counterpart appears in the 20CRv2 datasets, with a peak for wavenumber 2. However, the MJO has been highlighted as a phenomenon that climate models struggle to properly simulate, especially via underestimation of the OLR and representation of a coherent eastward propagation Kim et al.

The data used were the daily anomalies for the boreal winter November—April over the period — The daily anomalies were obtained by subtracting the climatological daily mean calculated over the period — Individual spectra were calculated for each boreal winter and then averaged over the time period used. The results are shown as the SLP anomalies regressed onto the corresponding normalized PC time series hectopascal per standard deviation for the period — Recent studies show that it has teleconnections to East Asia e.

Since the NAO's largest amplitude of variation occurs mainly during the boreal winter, the analyses presented here are centered on this season, and the period used to perform these analyses was — The variances explained by the leading EOF were also similar, Based on an analysis of the NAO variability, we propose that it is not necessary to analyze the Northern Annular Mode NAM , since both are manifestations of same mode of variability Hurrell and Deser, The time series used were from the boreal winter seasonal DJF averaged dataset for the period — Similar to the NAO, the PNA has its largest variation in amplitude during the boreal winter; therefore, the present analyses were performed for this season.

The time series used to perform the correlations were an averaged boreal winter seasonal DJF dataset over the period — The time series were departed from their long-term means and normalized at each grid point prior to the correlation computation. In that figure, it is possible to observe the four geopotential height centers simulated by the model, which show a stronger correlation when compared with the reanalysis correlation maps shown in Fig. These patterns have a significant impact on rainfall anomalies over South America Mo and Peagle, PSA patterns have significant interannual and decadal variabilities Zhang et al.

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The Southern Annular Mode SAM is the dominant atmospheric variability in the Southern Hemisphere, and it occurs in the extratropics and in the high latitudes Kidson, SAM variability is characterized by anomalous variations in the polar low pressure and in the surrounding zonally high-pressure belt. The SAM can be captured via the first EOF applied to different atmospheric variables, such as the sea level pressure, different geopotential height levels, and the surface air temperature Kidson, ; Rogers and van Loon, ; Thompson and Wallace, However, the explained variance is higher compared with the observation.

The observed SST anomalies over the North Pacific have shown an oscillatory pattern in the central and western parts in relation to the tropical part and along the North American west coast. The negative phase is a reversal of this pattern. Many studies have connected the PDO with variations in precipitation regimes in different regions around the world, including the South China monsoon e.

Following its definition Mantua et al.


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Although the EOF2 resembles the PDO mode, the tropical part has weaker variation compared with the observed variation. The basis for the model's deficiency in reproducing the PDO as the leading mode of variability is probably the model's simulation of weaker ENSO variability, both on spatial and temporal scales. It is possible to note that both time series present a multidecadal periodicity, but on different timescales, as confirmed by the power spectrum Fig.

The results are shown as the monthly SST anomalies regressed onto the corresponding normalized PC time series degrees Celsius per standard deviation over the period — The solid black lines show the 5-year running average. The ability of Earth system models to project future climate parameters based on conditions given by future scenarios of atmospheric greenhouse gas concentrations can be assessed by how accurately the models can reproduce observed climate features.

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Therefore, evaluation of how these models perform over historical periods for which there are observations that can be compared with model calculations represents a key part of Earth system modeling. The analysis of the mean climate showed that the model can simulate the general mean climate state.

Nevertheless, some significant biases appeared in the simulation, such as a double ITCZ over the Pacific and Atlantic oceans and some notable regional biases in the precipitation field e. This was the case for the tropical Atlantic mode of interhemispheric variability AMM , which was very well simulated by the model in terms of the spatial pattern and temporal variability.

The ability of the model to simulate the AMM and SACZ is an important result, since one of our main aims is to represent the modes that directly impact the precipitation over South America. Similar to Nobre et al. Regarding the atmospheric model, new developments have been carried out to improve BAM's capacity, with more sophisticated physics as described by Figueroa et al.

This new BESM version confronts the challenge of improving the precipitation simulation, in particular alleviating the deficit over the Amazon. The ENSO is a large-scale phenomenon that will be scrutinized to understand the reasons for weak variability. The other feature of the model is the weaker warming when the CO 2 equivalent is used as the only forcing compared with the warming predicted by other CMIP5 models that do not consider the direct and indirect effects of atmospheric aerosols.

Because models can respond in different ways to external forcing, an aim in the near future is to carry out a numerical experiment in which the model is forced with observed aerosol concentration estimates as a read-in field to address to what extent BESM is affected. Such a study will provide a broader perspective on the technical challenges overcame throughout this project and will assess the improvements achieved in each version of the model for better simulating the climate system.

Please contact Paulo Nobre paulo. SFV conducted the analyses and wrote the paper, under the supervision of PN.


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  8. VC and MBJ conducted the experiments. All of the authors contributed to the revision of the paper. Sandro F. Manoel Baptista Jr. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led the development of the software infrastructure, in partnership with the Global Organization for Earth System Science Portals.

    This work is part of the PhD dissertation of Sandro F. Veiga under the guidance of Carlos A. Nobre and Paulo Nobre. We thank the editor and three anonymous reviewers whose comments led to significant improvements of the paper. Adler, R. CO;2 , Ahn, M. Bentsen, M.