The performance of the coupled ocean–atmosphere component of the Brazilian Earth System Model version 2.5 (BESM-OA2.5) was evaluated in simulating the historical period 1850–2005. After a climate model validation procedure in which the main atmospheric and oceanic variabilities were evaluated against observed and reanalysis datasets, the evaluation specifically focused on the mean climate state and the most important large-scale climate variability patterns simulated in the historical run, which was forced by the observed greenhouse gas concentration. The most significant upgrades in the model's components are also briefly presented here. BESM-OA2.5 could reproduce the most important large-scale variabilities, particularly over the Atlantic Ocean (e.g., the North Atlantic Oscillation, the Atlantic Meridional Mode, and the Atlantic Meridional Overturning Circulation), and the extratropical modes that occur in both hemispheres. The model's ability to simulate such large-scale variabilities supports its usefulness for seasonal climate prediction and in climate change studies.
Climate models, which have recently been expanded into Earth system models via inclusion of biogeochemical cycles, are key tools for investigating climate phenomena that significantly influence human societies (e.g., von Storch, 2010; Flato, 2011). Since 2008, the Brazilian climate community has been engaged in setting up the Brazilian Earth System Model (BESM; Nobre et al., 2013; Giarolla et al., 2015). This major scientific task has been carried out by Brazilian scientific institutions and highlights the critical need to produce reliable future climate projections and to understand their potential impact, particularly over South America. The primary objective of this effort was to assemble the scientific expertise capable of developing and maintaining a state-of-the-art Earth system model. Such an achievement would represent a significant step forward in establishing a scientific tool that can be used in different types of research activities. The importance of such an undertaking lies in the need to understand the physics of the Earth system to produce and lend credibility to studies that explore the impacts of climate change on different areas of great importance, such as food and water security, tropical ecosystems, and natural disasters. One of the fundamental aims of the BESM project is to participate in the Coupled Model Intercomparison Project's sixth phase (CMIP6; Meehl et al., 2014).
BESM has been set up at the Brazilian National Institute for Space Research (INPE). Currently, it consists of a land–ocean–atmosphere coupled model in which the coupling is achieved via the FMS coupler, a tool developed at the Geophysical Fluid Dynamics Laboratory (GFDL) of the National Oceanic and Atmospheric Administration (NOAA). The inclusion of aerosols (as read-in fields) and atmospheric chemistry components is in the implementation and testing phases. Currently, work has been completed to activate the biogeochemical model, TOPAZ, within the Modular Ocean Model version 5 (MOM5) to simulate biogeochemical cycles in future simulations.
The previous version of BESM (BESM-OA2.3) was first evaluated by Nobre et al. (2013). This version showed a significant bias against precipitation in the tropical region, as it showed a deficient representation of the precipitation in the Amazon region. To improve these aspects, studies were conducted to ameliorate cloud parameterizations over the tropics, and the resulting changes improved the representation of the precipitation over the same region and the representation of Convergence Zones over both the Atlantic and Pacific Ocean basins (Bottino and Nobre, 2019). The main changes in the current version of BESM relate to its atmospheric model, which now incorporates modifications in the surface wind field and its parameterizations as described in Capistrano et al. (2018). The updated version presented in this paper is BESM-OA2.5.
Operationally, BESM-OA2.3 is already being used for extended weather forecasting (10–30 d) and for seasonal climate prediction (3 months), as well as for producing global climate change scenarios (Nobre et al., 2013) and providing atmospheric and oceanic boundary conditions to regional climate models for dynamical downscaling of climate change scenarios (Chou et al., 2014).
This overview paper describes the most important developments and improvements in the model's components, and presents the simulation of recent-past mean climate conditions and major large-scale climate phenomena. In Sect. 2, the BESM-OA2.5 components and experimental design are briefly described; Sect. 3 presents the methodology and the observed data used to evaluate the model; Sect. 4 presents the evaluation of the historical simulation, which evaluated the most important atmospheric and oceanic variables related to their climatological fields and the prominent large-scale phenomena of the climate system; and, finally, Sect. 5 provides a summary.
The atmospheric component of BESM-OA2.5 is the Brazilian Global Atmospheric Model (BAM;
Figueroa et al., 2016), which was developed at the Center for Weather Forecasting and
Climate Studies (CPTEC/INPE). The BAM is a primitive equation model with spectral
representation with triangular truncation at the wavenumber 62 (corresponding to a grid
resolution of approximately
The oceanic component of BESM-OA2.5 is the Modular Ocean Model version 4p1 (MOM4p1;
Griffies, 2009) developed at GFDL, which includes the Sea Ice Simulator (SIS) built-in
ice model (Winton, 2000). There were no changes in the physics parameterizations used in
BESM-OA2.3. The horizontal grid resolution in the zonal direction is 1
The atmospheric and oceanic models were coupled via the FMS coupler, which was also developed at GFDL and incorporated into MOM4p1. The atmospheric model receives sea surface temperature (SST) and ocean albedo data from the ocean and sea ice models at hourly time increments. The oceanic model receives information about freshwater (liquid and solid precipitation), momentum fluxes (winds at 10 m), specific humidity, heat, vertical diffusion of velocity components, and surface pressure, also at hourly time increments. The wind stress fields were computed in MOM4p1 using the Monin–Obukhov scheme (Obukhov, 1971). In the coupled simulations, the ocean temperature and salinity restoration options were set to off.
A set of numerical experiments were performed with the coupled
ocean–atmosphere version of BESM-OA2.5 following the CMIP5 experimental
design protocol (Taylor et al., 2012), and they are shown schematically in
Fig. 1. Out of the experiments listed below, only the historical simulation
is evaluated in this paper. The following experiments were performed:
The ocean stand-alone component ran for 71 years (a 13-year period of ocean model spin-up
forced by climatological atmospheric fields plus a 58-year period forced by interannually
varying atmospheric fields). Next, a spin-up of the fully coupled model was performed for
100 years. The oceanic and atmospheric states at the end of this 100-year-long
integration were used as the initial conditions for the piControl simulation. The
versions of the model differ slightly in the 100-year spin-up and the piControl run,
i.e., in the parameterizations of the land ice albedo and in the cloud microphysics. For
its initial conditions, the historical simulation used information about the 14th year
provided by the piControl simulation. The piControl simulation showed stable conditions
following a fast adjustment over the first 13 years of simulation (figure not shown).
Therefore, it is assumed that the historical simulation had a spin-up of 113 years. The
analyses of the piControl and 4xCO2 simulations are described in Capistrano el al. (2018). Capistrano et
al. (2018) estimated that BESM-OA2.5 has an equilibrium climate sensitivity of
2.96
A scheme of the principal simulations carried out by BESM-OA2.5 using different forcing conditions based on the CMIP5 protocols. The dates for the historical and RCP simulations are from the actual calendar years.
To evaluate the output of the BESM-OA2.5 historical simulation, comparisons were made
against the observed datasets and reanalysis products. The atmospheric fields and sea ice
concentration were from the Twentieth Century Reanalysis dataset version 2 (20CRv2; Compo
et al., 2011) with a global horizontal resolution of
To identify the main modes of climate variability, all of the analyses presented in the paper were performed using detrended dataset anomalies. Detrended datasets were obtained by removing the linear trend based on a least-squares regression. For the analyses using monthly datasets, the annual cycle was removed by subtracting the climatological monthly means from the respective individual months. Prior to performing the analyses, the model's datasets were interpolated to the grid resolution of the respective observation or the reanalysis datasets used for comparison.
The empirical orthogonal function analysis (EOF; Hannachi et al., 2007) was used to analyze the model's ability to simulate major modes of climate variability and to compare them with observations. Prior to performing the EOF calculations, the data were weighted by the square root of the cosine of latitude. The results of the EOF maps are shown as the original data anomalies regressed onto the normalized principal component (PC) time series, i.e., by the standard deviation.
In this paper, to evaluate the periodicity of the phenomena, the power spectrum technique based on Fourier analysis of the normalized time series was applied, in which the normalization was based on the long-term monthly standard deviation.
To gain better insight into the performance of BESM-OA2.5 in relation to the global
average near-surface air temperature and the average SST in the equatorial regions of the
Pacific and Atlantic Ocean, a comparison with 11 CMIP5 models was performed (Table 1).
Because the BESM-OA2.5 historical simulation is forced only by the observed
List of the models from CMIP5 with historical GHG simulations used for the comparison with BESM-OA2.5. Models with higher resolution in the tropical region and decreasing resolution towards the poles have two values for latitude in their respective oceanic resolution columns. For models with oceanic tripolar grids, the number of grid points in each coordinate are given.
In this section, the most important atmospheric and oceanic variables are evaluated in relation to their climatological fields, either globally or over regions in which their representations are key elements of the climate system.
The evolution of the global surface air temperature during the industrial era is a key element for analyzing the long-term model behavior while being forced by the observed conditions. The HadCRUT4 observation and BESM-OA2.5 time series of the globally averaged air temperature anomaly at 2 m are shown in Fig. 2. The time series are the annual mean anomalies relative to the period from 1850 to 1879. The BESM-OA2.5 simulation of the global average surface air temperature evolution closely followed the observed time series. However, since BESM-OA2.5 does not incorporate the representation of aerosols, and consequently their cooling effects, the surface air warming rate should be higher, similar to the remaining models (the grey shading in Fig. 2). To compare BESM-OA2.5 with the selected CMIP5 models, the grey shading represents the spread of the minimum and the maximum values of the yearly anomalies from the 11 models (Table 1). In this comparison, the historical GHG simulation was used, in which the models are only forced by well-mixed greenhouse gases (mainly carbon dioxide, methane, and nitrous oxide), without the cooling resulting from the direct and indirect effects of aerosols, volcanos, and effects of land use change. Thus, the CMIP5 models show a warmer tendency compared with the observations (see Jones et al., 2013, for more details). Although BESM-OA2.5 has the same forcing conditions, it does not show the warming tendency seen in the remaining models. With the exception of GFDL-ESM2M (1861–2005) and HadGEM2-ES (1860–2005), all of the remaining CMIP5 models encompass the period from 1850 to 2005, and their respective anomalies are from the period 1850 to 1879. For GFDL-ESM2M and HadGEM2-ES, the anomalies are computed relative to the periods 1861–1890 and 1860–1889, respectively.
Global averaged 2 m annual mean air temperature anomalies relative to the period 1850–1879 as simulated by BESM-OA2.5 (dashed red line) and observed (solid black line). The grey shading represents the spread of 11 CMIP5 models (historical GHG simulations). The CMIP5 model anomalies were also computed relative to the period 1850–1879, with the exception of GFDL-ESM2M and HadGEM2-ES whose anomalies were computed relative to the periods 1861–1890 and 1860–1889, respectively. Units are in degrees Celsius.
The net radiation at the top of atmosphere (TOA) has a negative bias and the
net ocean/atmosphere heat flux has a positive bias (Fig. 3). The net TOA
radiation has a mean value of
Annual average time series for the global average
One of the key points in evaluating a climate model is to gauge its ability to simulate
the hydrological cycle, as this cycle is critical for maintaining the energy balance of
the climate system. Figure 4 shows the spatial distribution of annual mean precipitation
for (a) BESM-OA2.5, (b) the GPCP dataset, (c) the spatial distribution of annual mean
precipitation bias for BESM-OA2.5 relative to the GPCP dataset, and (d) for BESM-OA2.5
relative to the CMAP dataset. The spatial annual mean precipitation values represent
averaged values over the periods 1971–2000 and 1979–2008 for BESM-OA2.5, and the GPCP
and CMAP datasets, respectively. The global model's mean biases are similar for GPCP
(0.3 mm d
Spatial maps of the annual mean precipitation for
Spatial maps showing the averaged global anomalies in velocity potential and
wind divergence at the 200 hPa pressure level for
To understand the global atmospheric circulation associated with the precipitation deficiencies over both the Amazon and Indian regions, the global anomalies of the velocity potential and the divergence of the wind at the 200 hPa pressure level were computed and are shown in Fig. 5. The velocity potential and divergent wind anomalies were averaged over the period 1971–2000 for the BESM-OA2.5 outputs (Fig. 5a), the 20CRv2 reanalysis (Fig. 5b), and for the difference BESM-OA2.5 minus reanalysis (Fig. 5c, d, and e). Figure 5c shows anomalous convergence over the Amazonian and Indian regions resulting in the model's poor capacity in creating convection and, consequently, to generate precipitation. Figure 5d and e show the velocity potential and wind divergence separated by season. For the Amazonian rainfall season, which occurs during MAM, it is possible to observe anomalous convergence at high levels of the atmosphere (Fig. 5d). An equivalent result was observed for the Indian region during the JJA season (Fig. 5e).
Figure 6 shows the zonally averaged precipitation during the four seasons. For this
comparison, the results of the BESM-OA2.3 analysis performed by Nobre et al. (2013) are
also shown. Both versions could reproduce the maximum peaks of precipitation in both the
tropical and subtropical regions. BESM-OA2.5 showed a negative bias from around
40
Zonally averaged annual mean precipitation for the BESM-OA2.5, BESM-OA2.3, and GPCP datasets relative to the seasons DJF, MAM, JJA, and SON. The zonally averaged values were computed over the periods 1971–2000 and 1979–2008 for BESM-OA2.5 and GPCP, respectively. Units are in millimeters per day.
Figure 7 shows the general characteristics of cloudiness over the globe simulated by the
model. In particular, Fig. 7a shows that the model underestimated cloudiness in most
parts of the globe, with significant exceptions in the high latitudes of the boreal
hemisphere and in the southern subequatorial regions of the Pacific and Atlantic oceans
upon comparison with observations. Globally, BESM-OA2.5 has a cloudiness negative bias of
Figures 8 and 9 present the analysis of the zonally averaged vertical profiles of air
temperature and zonal wind for all seasons as simulated by BESM-OA2.5 and the respective
bias relative to the 20CRv2 reanalysis dataset, in which all of the data are time
averaged over the period 1971–2000. BESM-OA2.5 had a large positive air temperature bias
that appears above the 250 hPa pressure level (Fig. 8) in the subpolar and polar regions
during all of the seasons. This result indicates that the model warms abnormally in the
tropopause and the lower stratosphere in the polar regions. The warm bias is stronger
during the DJF and MAM seasons over the northern polar region, reaching a maximum bias of
20
Contour lines showing the zonally averaged vertical air temperatures for
BESM-OA2.5 and the difference between the BESM-OA2.5 and 20CRv2 datasets are shaded. Both
are averaged over the period 1971–2000. The units are in degrees Celsius and the contour
interval is 10
Contour lines showing the zonally averaged zonal wind for BESM-OA2.5 and the
differences between the BESM-OA2.5 and 20CRv2 datasets are shaded. Both datasets were
averaged over the period 1971–2000. The solid contour lines represent eastward zonal
wind and the dashed contour lines represent westward zonal wind. The units are in meters
per second and the contour interval is 5 m s
Concerning the zonal wind, BESM-OA2.5 simulated a much weaker wind speed in
the tropopause and stratosphere over the boreal hemisphere, mainly during the
DJF season, which has a maximum negative bias of
Spatial maps of the annual mean sea surface temperatures generated by
The global distribution and the range values of SST are important characteristics of the
mean climate state. Figure 10 shows a spatial map of the annual mean SST values for
(a) BESM-OA2.5 and (b) ERSSTv4 as well as (c) the bias for BESM-OA2.5 relative to the
ERSSTv4 dataset. BESM-OA2.5 showed a warm SST bias that spread throughout all of the
oceans, in contrast with the negative biases shown by most of the CMIP5 models over the
North Pacific and North Atlantic oceans (see Wang et al., 2014). However, the extreme
values found in the south of Greenland and in the North Pacific, where they reached
Figure 11a shows the mean SSTs in the equatorial Pacific for BESM-OA2.5 and
ERSSTv4, averaged over the period 1971–2000. The equatorial region is
defined as the region lying between the latitudes 2
As Fig. 11, but for the Atlantic Ocean.
To evaluate how the global ocean profile evolves throughout the simulation, depth–time
Hovmöller diagrams of global mean ocean salinity and temperature departures from
their respective initial conditions were calculated (Fig. 13a and b) in the historical
simulation. Here, “initial condition” indicates the value of the first year of the
simulation, in this case 1850. The ocean salinity slightly increased below a depth of
1000 m and from 1935 on, the increase reached 0.04 PSU between depths of 1500 and
3000 m compared with the initial values (Fig. 13a). Above a depth of 1000 m, there was
a significant freshening of the ocean waters, with the surface water salinity decreasing
up to 0.18 PSU by the end of the simulation. Concerning ocean temperature, prominent
warming occurred from the surface up to a depth of 400 m (Fig. 13b). This warming was
more significant at the end of the simulation (
Depth–time Hovmöller diagrams of the global average ocean
The meridional overturning circulation (MOC) plays an important role in transporting heat
from the tropics to the higher latitudes in both hemispheres. 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, 2016). The AMOC in the BESM-OA2.5 historical experiment showed the typical
structure described in Lumpkin and Speer (2007): the upper layer of the upper cell, which
is the northward flux, depicted at the appropriate depth, from the surface down to
After averaging the maximum AMOC strength over the first and the last 30 years of the time series, i.e., over the periods 1850–1879 and 1976–2005, respectively, the result shows a decrease of 11.2 %, from 16.9 to 15.1 Sv during each period. 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 1.4 Sv.
Figure 15 shows the mean sea ice concentration simulated by BESM-OA2.5 for the end of the winter and the summer seasons for each hemisphere over the period 1971–2000. The thick black lines represent the 15 % climatological values for the period 1971–2000 given by the 20CRv2 reanalysis. The sea ice concentration at the end of the Arctic winter was overestimated in the Atlantic, specifically north of Scandinavia (Fig. 15a). However, at the end of the Arctic summer, the sea ice concentration was underestimated (Fig. 15b). At the end of the Antarctic summer, the model showed a significant underestimation of the sea ice concentration (Fig. 15c), whereas at the end of the Antarctic winter, the model generally overestimated the extension of the sea ice concentration over the Southern Ocean (Fig. 15d). 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.
BESM-OA2.5 mean sea ice concentrations for March
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.
The El Niño–Southern Oscillation (ENSO) in the equatorial Pacific Ocean is one of
the most prominent climate variability phenomena on interannual timescales (Dijkstra,
2006), and it has strong effects on regions surrounding the Indian
Ocean and Pacific Ocean and regions that are
influenced by its teleconnections (see McPhaden et al., 2006, and references therein).
There are many methods to evaluate the ENSO variability. In the present study, the EOF
was applied to detrended monthly SST anomalies over the tropical Pacific Ocean
(30
The leading EOF modes of the detrended monthly SST anomalies over the tropical
Pacific region (30
Figure 17 shows the spatial correlation between the detrended monthly anomalies of the
Niño-3 index (defined inside the black rectangular area, bounded by
5
Spatial maps with the monthly correlations between the Niño-3 index and the
global SST anomalies computed by
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, 1993; Lutz et al., 2015), and the meridional mode, also referred to as the interhemispheric mode (Nobre and Shukla, 1996). 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, 1993; Lutz et al., 2015). 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, 1996; Chang et al., 1997; Chiang and Vimont, 2004). 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, 1994; Chang et al., 1997; Xie, 1999). To evaluate the AMM simulations, a joint EOF of SST and wind stress (Taux and Tauy) fields was computed, as such variability is intrinsic to the coupled ocean–atmospheric system. Figure 18 shows the AMM simulated by BESM-OA2.5 and that obtained via observed data. The AMM pattern simulated by the model is similar to that obtained from observations, regardless of the weaker gradient pole in the South Atlantic. Nevertheless, the variance explained by the model (10.7 %) is very close to the observed value (11.8 %). The patterns shown in Fig. 18 are defined as a positive phase of the AMM, with the interhemisphere cross-equatorial wind from the south to the north and with corresponding negative SST anomalies over the southern pole and positive SST anomalies over the northern pole (the negative phase of the AMM is the reverse pattern). Over the second half of the twentieth century, the AMM showed a predominant decadal periodicity of 11–13 years. Figure 18c and d show the power spectra of the PC of the AMM patterns simulated by the model and based on observed data, respectively. It is possible to see that the pattern simulated by BESM-OA2.5 shows, similar to that derived from the observed data, a predominant periodicity on decadal timescales.
The leading joint EOF modes of the detrended monthly SST and wind stress (Taux
and Tauy) anomalies for the tropical Atlantic region (30
The South Atlantic Convergence Zone (SACZ) is characterized by an intense NW–SE-oriented
cloud band that extends from the Amazon Basin to the South Atlantic subtropics, mainly
during the austral summer (Nogués-Paegle and Mo, 1997; Carvalho et al., 2004; de
Oliveira Vieira et al., 2013). 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, 2002). The southern part of the SACZ normally lies
over cooler SSTs (Grimm, 2003; Robertson and Mechoso, 2000). Chaves and Nobre (2004)
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.
atmospheric general circulation models (AGCMs) are unable to simulate the
precipitation over the cooler SSTs caused by the SACZ (Marengo et al., 2003; Nobre et
al., 2006, 2012) since such models tend to increase the precipitation over warmer SSTs as
a hydrostatic response. Nobre et al. (2012) showed that coupled
atmosphere–ocean general circulation models (AOGCMs) can simulate SACZ formation over
colder SST anomalies, as this class of models incorporates atmosphere–ocean surface
thermodynamic coupling. Following Nobre et al. (2012), a correlation exists between the
seasonal precipitation and SST anomalies during the austral summer (DJF) over the
tropical South Atlantic (40
Spatial maps with the correlation between SST and precipitation (seasonal
average DJF) over the South Atlantic Ocean (40
The Madden–Julian oscillation (MJO) is the primary intraseasonal variability (30–90 d)
over the eastern Indian Ocean and western
Pacific tropical regions and consists of deep convection events coupled to atmospheric
circulation that propagate together eastward through the equatorial region (Madden and
Julian, 1971, 1972; Zhang, 2005). The influence of MJO events on large-scale phenomena
has been reported, as in the case of the evolution of ENSO (e.g., Takayabu et al., 1999),
with the formation of tropical cyclones (e.g., Liebmann et al., 1994) and in the North
Atlantic Oscillation (e.g., Lin et al., 2009). To evaluate the MJO simulated by the
model, wavenumber–frequency power spectrum analyses were performed for tropical
(10
Figure 20a and b show the wavenumber–frequency power spectra for the OLR from BESM-OA2.5
and 20CRv2, respectively. Although BESM-OA2.5 yielded an eastward-propagating disturbance
with wavenumber 1, it was characterized by a lower frequency (
Wavenumber–frequency power spectra of the tropical
(10
The leading EOF modes of the boreal winter (DJF) seasonal averaged sea level
pressure (SLP) anomalies for the Euro-Atlantic region (20–80
The North Atlantic Oscillation (NAO) is a major atmospheric variability pattern that
occurs in the North Atlantic that is characterized by oscillations in the sea level
pressure (SLP) differences between Iceland and Portugal (Wanner et al., 2001; Hurrel et
al., 2003). The NAO has a robust impact in the Euro-Atlantic region (Hurrell et al.,
2003; Hurrell and Deser, 2009), and the notable work of Namias (1972) connected the droughts in northeast Brazil to NAO
variations. Recent studies show that it has teleconnections to East Asia (e.g., Yu and
Zhou, 2004; Wu et al., 2012). The NAO's influence on rapid climate changes in the
Northern Hemisphere has been highlighted in Delworth et al. (2016), thus making its
correct simulation more critical. 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 1950–2005. The leading EOF of the SLP
averaged over the boreal winter season (DJF) in the Euro-Atlantic region showed that the
NAO is well simulated by BESM-OA2.5 (Fig. 21a), as its simulations of the NAO dipole
centers and their amplitudes were very similar to the observed pattern (Fig. 21b). The
variances explained by the leading EOF were also similar, 50.2 % and 44 % for
BESM-OA2.5 and the 20CRv2 reanalysis, respectively. The spectral analysis of the leading
PCs showed that BESM-OA2.5 captures the
Together, the NAO and the Pacific–North American pattern (PNA) are the dominant atmospheric internal modes over the boreal hemisphere. The PNA is characterized by four centers of the 500 hPa geopotential height anomalies in the North Pacific and over North America: centers located over Hawaii, in the south of the Aleutian Islands, in the intermountain region of North America, and in the Gulf Coast region of the USA, representing the centers of action of a stationary wave train extending from the tropical Pacific into North America (Wallace and Gutzler, 1981). The PNA exerts a significant influence on surface temperature and precipitation over North America (Leathers et al., 1991). Some studies have shown that although the PNA is an internal atmospheric variability phenomenon, it is influenced by other climate variabilities, including the ENSO and the Pacific Decadal Oscillation (PDO; see Straus and Shukla, 2002; Yu and Zwiers, 2007).
One-point correlation maps for
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. Following Wallace
and Gutzler (1981), we constructed one-point correlation maps for BESM-OA2.5 and the
20CRv2 reanalysis to evaluate the capacity of the model to reproduce the PNA pattern. The
one-point correlation maps correlate the 500 hPa geopotential height at the reference
point (45
The second and third EOF of the 500 hPa geopotential height over the Southern Hemisphere
(20–90
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, 1988).
It is also referred to as the Antarctic Oscillation (AAO; Gong and Wang, 1999). 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, 1988; Rogers and van
Loon, 1982; Thompson and Wallace, 2000). To evaluate the capacity of BESM-OA2.5 to simulate this atmospheric mode of
variability, EOF analysis was applied to the monthly mean 500 hPa geopotential height
field from 20 to 90
The leading EOF modes of the monthly mean 500 hPa geopotential
height field for the Southern Hemisphere (20–90
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. This oscillatory shift in SST anomalies with interdecadal periodicity was termed the Pacific Decadal Oscillation (PDO), and it is defined as the leading EOF of the monthly SST anomalies over the North Pacific (Mantua et al., 1997). The positive phase of the PDO is defined when negative SST anomalies are predominate over the central and western parts of North Pacific and positive SST anomalies predominate over the tropical Pacific 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.g., Wu and Mao, 2016), the Indian monsoon (e.g., Krishnamurthy and Krishnamurthy, 2016), and, together with the ENSO, the precipitation regime in North America (see Hu and Huang, 2009). There are different mechanisms that modulate the PDO, among which is the response of the Northern Pacific SST to the ENSO variability via the “atmospheric bridge” (for a detailed review, see Newman et al., 2016).
Following its definition (Mantua et al., 1997), the spatial pattern of the PDO was
obtained by regressing the SST anomalies onto the leading normalized PC time series, as
shown in Fig. 25, which in this case shows the positive phase of the PDO. The EOF was
applied to monthly SST anomalies over the North Pacific (20–60
Normalized second PC time series for
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. 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. In this study, the BESM-OA2.5 historical simulation was evaluated for the period 1850–2005 following the CMIP5 protocol (Taylor et al., 2012) with a focus on simulations of the mean climate and key large-scale modes of climate variability.
BESM-OA2.5 is an updated version of BESM-OA2.3 (Nobre et al., 2013; Giarolla et al.,
2015), which now incorporates the new Brazilian Global Atmospheric Model (BAM; Figueroa
et al., 2016). This new version reduced a mean global bias of the energy balance at the
top of the atmosphere from
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.g., over the Amazon and Indian regions) and in the SST field (e.g., south of Greenland). Nevertheless, the model has shown an improvement in simulating the ITCZ and a reduction in the global precipitation RMSE compared with that of BESM-OA2.3. BESM-OA2.5 shows a nearly globally positive SST bias that was absent in version 2.3; however, the SST RMSE was slightly reduced in the newer version of the model.
The most relevant climate patterns on interannual to decadal timescales simulated by BESM-OA2.5 were compared with the ones obtained from observations and reanalysis. Over the Pacific, the ENSO was simulated with a lower amplitude of variability than that recorded from the observations, and this weak ENSO seems to impact other Pacific variability patterns, such as the PDO. Conversely, the major phenomena over the Atlantic basin were well represented in BESM-OA2.5 simulations. 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. It is worth noting that this mode is considered to be poorly simulated by the models used in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5; Flato et al., 2013). It is also relevant to highlight the ability of BESM-OA2.5 to represent the enhanced rainfall over the cooler waters of the SW tropical Atlantic that are associated with the South Atlantic Convergence Zone (SACZ). 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. The AMOC reproduced by BESM-OA2.5 has a meridional overturning structure comparable with the ensemble AMOC simulated by the CMIP5's models. BESM's maximum AMOC strength average value was slighter lower than the average value observed by the RAPID project, but well within the range of the observed root-mean-square variability. Although the averaged maximum strength AMOC simulated by the CMIP5 models is within the observed root-mean-square variability range, most models tend to simulate a strong AMOC, with a maximum strength above 20 Sv, which is outside of the range (Zhang and Wang, 2013). The NAO atmospheric variability, which is well simulated by the CMIP5 models (Ning and Bradley, 2016), is also very well simulated by BESM-OA2.5. In the extratropics, BESM-OA2.5 could reproduce major variabilities in both hemispheres, such as the PNA, PSA, and the SAM teleconnection patterns, relatively well compared to the CMIP5 models, which reproduces the PNA (Ning and Bradley, 2016) and SAM (Zheng et al., 2013).
Similar to Nobre et al. (2013), this study aimed to evaluate BESM-OA2.5 by comparing the
most important features of the climate system simulated by the model with observations
and reanalysis. The next version of the model (BESM-OA2.9) is already under development.
In this new version, the MOM4p1 ocean model has been replaced by the MOM5. 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., 2016). 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
The BESM-OA2.5 source code is freely available after signing a license agreement. Please contact Paulo Nobre (paulo.nobre@inpe.br) to obtain the BESM-OA2.5 source code and data.
SFV conducted the analyses and wrote the paper, under the supervision of PN. PN, EG, VC, MBJ, ALM, SNF, JPB, and PK worked in the development of the new version of the model. VC and MBJ conducted the experiments. All of the authors contributed to the revision of the paper.
The authors declare that they have no conflict of interest.
This research was partially funded by FAPESP (2009/50528-6), FAPESP (2008/57719-9), and by the National Institute of S&T for Climate Change (CNPq 573797/2008-0). Sandro F. Veiga is supported by a PhD grant funded by CAPES. Manoel Baptista Jr. is supported by a grant funded by FAPESP (2018/06204-0). The authors would like to acknowledge Rede CLIMA, FAPESP, and INPE for the use of their supercomputer facility, which made this work possible. The Twentieth Century Reanalysis project datasets (20CRv2) were provided by the U.S. Department of Energy, Office of Science Innovative and Novel Computational Impact on Theory and Experiment (DOE INCITE) program, the Office of Biological and Environmental Research (BER), and the National Oceanic and Atmospheric Administration Climate Program Office. The GPCP combined precipitation datasets were developed and computed by the NASA/Goddard Space Flight Center's Mesoscale Atmospheric Processes Laboratory. The HadCRUT4 data sets were provided by the Met Office Hadley Centre and the University of East Anglia/Climatic Research Unit. The ISCCP D2 datasets were provided through the International Satellite Cloud Climatology Project, maintained by the ISCCP research group at the NASA/Goddard Institute for Space Studies. The Extended Reconstructed Sea Surface Temperature (ERSSTv4) data were provided by the NOAA/OAR/ESRL/PSD. The data from the RAPID-WATCH MOC monitoring project were funded by the Natural Environment Research Council. The authors acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. For CMIP, the U.S. 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.
This paper was edited by Qiang Wang and reviewed by three anonymous referees.